Pub Date : 2024-11-01Epub Date: 2024-11-19DOI: 10.3390/technologies12110232
Elsa M Orellano-Colón, Adriana I Ramos-Marichal, Valeria R González-Crespo, Bianca N Zeballos-Hernández, Kenneth N Ruiz-Márquez, Abiel Roche-Lima, Joan M Adorno-Mercado, Norman A Laborde-Torres, Joshua G Berríos-Llopart, Angely M Cruz-Ramos, Dana V Montenegro, Carmen E Lamoutte, Natasha D Rosa-Casilla, David E Meléndez-Berrios
Latinos are among the populations who are the least likely to use assistive technology (AT) despite being a population with a high prevalence of functional disabilities (FDs). We aimed to create and test the usability of an AT web app for independent-living older adults with FDs. In Phase I, we created the web app's content guided by the Optimized Honeycomb Model and considered the AT needs and FDs of older Puerto Ricans found in our previous studies. In Phase II, we design the web application by adopting a Lean UX process and design heuristics for older adults. In Phase III, we conducted usability testing using focus groups and individual interviews with 14 older adults, interpreted through a directed content analysis. The Mi Guía de Asistencia Tecnológica (MGAT) was developed with ninety-four AT devices in eight areas of daily activities. The MGAT provides comprehensive information on AT, including photos and videos of older adults using AT. Participants reported that the MGAT was usable, accessible, credible, desirable, useful, and valuable in increasing their knowledge of AT. These findings are a foundation for developing efficient AT information strategies using such technology as a first step to improving AT adoption among older adults.
拉丁美洲人是最不可能使用辅助技术（AT）的人群之一，尽管他们是功能性残疾（fd）高发的人群。我们的目标是为患有fd的独立生活的老年人创建并测试一个AT网络应用程序的可用性。在第一阶段，我们在优化蜂巢模型的指导下创建了web应用程序的内容，并考虑了我们之前研究中发现的波多黎各老年人的AT需求和fd。在第二阶段，我们通过采用精益UX流程和针对老年人的设计启发式来设计web应用程序。在第三阶段，我们使用焦点小组和14名老年人的个人访谈进行可用性测试，并通过直接内容分析进行解释。Mi Guía de Asistencia Tecnológica （MGAT）是在八个日常活动领域开发的94个AT设备。MGAT提供了有关AT的全面信息，包括老年人使用AT的照片和视频。参与者报告说，MGAT是可用的、可获得的、可信的、可取的、有用的，并且在增加他们对AT的了解方面有价值。这些发现是开发有效的信息策略的基础，使用这种技术作为提高老年人采用自动驾驶技术的第一步。
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Pub Date : 2024-01-08DOI: 10.3390/technologies12010008
Mohammed Mahmoud
Big Data analysis is one of the most contemporary areas of development and research in the present day [...]
大数据分析是当今最前沿的发展和研究领域之一 [...]
{"title":"Editorial for the Special Issue “Data Science and Big Data in Biology, Physical Science and Engineering”","authors":"Mohammed Mahmoud","doi":"10.3390/technologies12010008","DOIUrl":"https://doi.org/10.3390/technologies12010008","url":null,"abstract":"Big Data analysis is one of the most contemporary areas of development and research in the present day [...]","PeriodicalId":101448,"journal":{"name":"Technologies","volume":"42 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139446443","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 : 2024-01-05DOI: 10.3390/technologies12010006
Fu-Cheng Wang, Hsiao-Tzu Huang
This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of prediction models and corresponding changes in system parts and power management based on the accumulated data. We first develop a hybrid power model to evaluate system responses under different conditions. We then build prediction models using five artificial intelligence algorithms. Among them, the light gradient boosting machine and extreme gradient boosting methods achieve the highest accuracies for predicting solar radiation and load responses, respectively. Therefore, we apply these two models to forecast solar and load responses. Third, we introduce extended-window algorithms and investigate the effects of window sizes and replacement costs on system performance. The results show that the optimal window size is one week, and the system cost is 13.57% lower than the cost of the system that does not use the extended-window algorithms. The proposed method also tends to make fewer component replacements when the replacement cost increases. Finally, we design experiments to demonstrate the feasibility and effectiveness of systems using extended-window model prediction.
{"title":"Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems","authors":"Fu-Cheng Wang, Hsiao-Tzu Huang","doi":"10.3390/technologies12010006","DOIUrl":"https://doi.org/10.3390/technologies12010006","url":null,"abstract":"This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of prediction models and corresponding changes in system parts and power management based on the accumulated data. We first develop a hybrid power model to evaluate system responses under different conditions. We then build prediction models using five artificial intelligence algorithms. Among them, the light gradient boosting machine and extreme gradient boosting methods achieve the highest accuracies for predicting solar radiation and load responses, respectively. Therefore, we apply these two models to forecast solar and load responses. Third, we introduce extended-window algorithms and investigate the effects of window sizes and replacement costs on system performance. The results show that the optimal window size is one week, and the system cost is 13.57% lower than the cost of the system that does not use the extended-window algorithms. The proposed method also tends to make fewer component replacements when the replacement cost increases. Finally, we design experiments to demonstrate the feasibility and effectiveness of systems using extended-window model prediction.","PeriodicalId":101448,"journal":{"name":"Technologies","volume":"45 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139382316","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 : 2024-01-05DOI: 10.3390/technologies12010007
Jaime-Rodrigo González-Rodríguez, Diana-Margarita Córdova-Esparza, Juan R. Terven, J. Romero-González
People with hearing disabilities often face communication barriers when interacting with hearing individuals. To address this issue, this paper proposes a bidirectional Sign Language Translation System that aims to bridge the communication gap. Deep learning models such as recurrent neural networks (RNN), bidirectional RNN (BRNN), LSTM, GRU, and Transformers are compared to find the most accurate model for sign language recognition and translation. Keypoint detection using MediaPipe is employed to track and understand sign language gestures. The system features a user-friendly graphical interface with modes for translating between Mexican Sign Language (MSL) and Spanish in both directions. Users can input signs or text and obtain corresponding translations. Performance evaluation demonstrates high accuracy, with the BRNN model achieving 98.8% accuracy. The research emphasizes the importance of hand features in sign language recognition. Future developments could focus on enhancing accessibility and expanding the system to support other sign languages. This Sign Language Translation System offers a promising solution to improve communication accessibility and foster inclusivity for individuals with hearing disabilities.
{"title":"Towards a Bidirectional Mexican Sign Language–Spanish Translation System: A Deep Learning Approach","authors":"Jaime-Rodrigo González-Rodríguez, Diana-Margarita Córdova-Esparza, Juan R. Terven, J. Romero-González","doi":"10.3390/technologies12010007","DOIUrl":"https://doi.org/10.3390/technologies12010007","url":null,"abstract":"People with hearing disabilities often face communication barriers when interacting with hearing individuals. To address this issue, this paper proposes a bidirectional Sign Language Translation System that aims to bridge the communication gap. Deep learning models such as recurrent neural networks (RNN), bidirectional RNN (BRNN), LSTM, GRU, and Transformers are compared to find the most accurate model for sign language recognition and translation. Keypoint detection using MediaPipe is employed to track and understand sign language gestures. The system features a user-friendly graphical interface with modes for translating between Mexican Sign Language (MSL) and Spanish in both directions. Users can input signs or text and obtain corresponding translations. Performance evaluation demonstrates high accuracy, with the BRNN model achieving 98.8% accuracy. The research emphasizes the importance of hand features in sign language recognition. Future developments could focus on enhancing accessibility and expanding the system to support other sign languages. This Sign Language Translation System offers a promising solution to improve communication accessibility and foster inclusivity for individuals with hearing disabilities.","PeriodicalId":101448,"journal":{"name":"Technologies","volume":"38 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139382496","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 : 2024-01-03DOI: 10.3390/technologies12010005
Pau Figuera, Pablo García Bringas
This manuscript provides a comprehensive exploration of Probabilistic latent semantic analysis (PLSA), highlighting its strengths, drawbacks, and challenges. The PLSA, originally a tool for information retrieval, provides a probabilistic sense for a table of co-occurrences as a mixture of multinomial distributions spanned over a latent class variable and adjusted with the expectation–maximization algorithm. The distributional assumptions and the iterative nature lead to a rigid model, dividing enthusiasts and detractors. Those drawbacks have led to several reformulations: the extension of the method to normal data distributions and a non-parametric formulation obtained with the help of Non-negative matrix factorization (NMF) techniques. Furthermore, the combination of theoretical studies and programming techniques alleviates the computational problem, thus making the potential of the method explicit: its relation with the Singular value decomposition (SVD), which means that PLSA can be used to satisfactorily support other techniques, such as the construction of Fisher kernels, the probabilistic interpretation of Principal component analysis (PCA), Transfer learning (TL), and the training of neural networks, among others. We also present open questions as a practical and theoretical research window.
{"title":"Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and Insights","authors":"Pau Figuera, Pablo García Bringas","doi":"10.3390/technologies12010005","DOIUrl":"https://doi.org/10.3390/technologies12010005","url":null,"abstract":"This manuscript provides a comprehensive exploration of Probabilistic latent semantic analysis (PLSA), highlighting its strengths, drawbacks, and challenges. The PLSA, originally a tool for information retrieval, provides a probabilistic sense for a table of co-occurrences as a mixture of multinomial distributions spanned over a latent class variable and adjusted with the expectation–maximization algorithm. The distributional assumptions and the iterative nature lead to a rigid model, dividing enthusiasts and detractors. Those drawbacks have led to several reformulations: the extension of the method to normal data distributions and a non-parametric formulation obtained with the help of Non-negative matrix factorization (NMF) techniques. Furthermore, the combination of theoretical studies and programming techniques alleviates the computational problem, thus making the potential of the method explicit: its relation with the Singular value decomposition (SVD), which means that PLSA can be used to satisfactorily support other techniques, such as the construction of Fisher kernels, the probabilistic interpretation of Principal component analysis (PCA), Transfer learning (TL), and the training of neural networks, among others. We also present open questions as a practical and theoretical research window.","PeriodicalId":101448,"journal":{"name":"Technologies","volume":"51 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451929","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 : 2024-01-02DOI: 10.3390/technologies12010004
Prabu Pachiyannan, M. Alsulami, D. Alsadie, Abdul Khader Jilani Saudagar, Mohammed Alkhathami, R. C. Poonia
Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the model’s performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPM’s superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPM’s effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women.
{"title":"A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing","authors":"Prabu Pachiyannan, M. Alsulami, D. Alsadie, Abdul Khader Jilani Saudagar, Mohammed Alkhathami, R. C. Poonia","doi":"10.3390/technologies12010004","DOIUrl":"https://doi.org/10.3390/technologies12010004","url":null,"abstract":"Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the model’s performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPM’s superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPM’s effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women.","PeriodicalId":101448,"journal":{"name":"Technologies","volume":"129 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453394","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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