Vytautas Ostasevicius, Agnė Paulauskaite-Taraseviciene, Vaiva Lesauskaite, Vytautas Jurenas, Vacis Tatarunas, Edgaras Stankevicius, Agilė Tunaityte, Mantas Venslauskas, Laura Kizauskiene
In this study, we reveal the influence of low-frequency ultrasound on erythrocyte and platelet aggregation. Furthermore, we show that the consequences of sonication of blood samples can be predicted using machine learning techniques based on a set of explicit parameters. A total of 300 blood samples were exposed to low-frequency ultrasound of varying intensities for different durations. The blood samples were sonicated with low-frequency ultrasound in a water bath, which operated at a frequency of 46 ± 2 kHz. Statistical analyses, an ANOVA, and the non-parametric Kruskal–Wallis method were used to evaluate the effect of ultrasound on various blood parameters. The obtained results suggest that there are statistically significant variations in blood parameters attributed to ultrasound exposure, particularly when exposed to a high-intensity signal lasting 180 or 90 s. Furthermore, among the five machine learning algorithms employed to predict ultrasound’s impact on platelet counts, support vector regression (SVR) exhibited the highest prediction accuracy, yielding an average MAPE of 10.34%. Notably, it was found that the effect of ultrasound on the hemoglobin (with a p-value of < 0.001 for MCH and MCHC and 0.584 for HGB parameters) in red blood cells was higher than its impact on platelet aggregation (with a p-value of 0.885), highlighting the significance of hemoglobin in facilitating the transfer of oxygen from the lungs to bodily tissues.
{"title":"Prediction of Changes in Blood Parameters Induced by Low-Frequency Ultrasound","authors":"Vytautas Ostasevicius, Agnė Paulauskaite-Taraseviciene, Vaiva Lesauskaite, Vytautas Jurenas, Vacis Tatarunas, Edgaras Stankevicius, Agilė Tunaityte, Mantas Venslauskas, Laura Kizauskiene","doi":"10.3390/asi6060099","DOIUrl":"https://doi.org/10.3390/asi6060099","url":null,"abstract":"In this study, we reveal the influence of low-frequency ultrasound on erythrocyte and platelet aggregation. Furthermore, we show that the consequences of sonication of blood samples can be predicted using machine learning techniques based on a set of explicit parameters. A total of 300 blood samples were exposed to low-frequency ultrasound of varying intensities for different durations. The blood samples were sonicated with low-frequency ultrasound in a water bath, which operated at a frequency of 46 ± 2 kHz. Statistical analyses, an ANOVA, and the non-parametric Kruskal–Wallis method were used to evaluate the effect of ultrasound on various blood parameters. The obtained results suggest that there are statistically significant variations in blood parameters attributed to ultrasound exposure, particularly when exposed to a high-intensity signal lasting 180 or 90 s. Furthermore, among the five machine learning algorithms employed to predict ultrasound’s impact on platelet counts, support vector regression (SVR) exhibited the highest prediction accuracy, yielding an average MAPE of 10.34%. Notably, it was found that the effect of ultrasound on the hemoglobin (with a p-value of < 0.001 for MCH and MCHC and 0.584 for HGB parameters) in red blood cells was higher than its impact on platelet aggregation (with a p-value of 0.885), highlighting the significance of hemoglobin in facilitating the transfer of oxygen from the lungs to bodily tissues.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"41 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134905986","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}
Maria Stoettrup Schioenning Larsen, Astrid Heidemann Lassen, Casper Schou
Despite the promising potential of Industry 4.0, the transition of the manufacturing industry is still very slow-paced. In this article, we argue that one reason for this development is the fact that existing foundational process models of manufacturing innovation are developed for steady-state conditions, not considering the complexity and uncertainty related to Industry 4.0. This lack of models built for the characteristics of Industry 4.0 further translates into a lack of operational approaches and insights into engaging with Industry 4.0 in practice. Therefore, this article presents a case study of developing a comprehensive Industry 4.0 solution and identifies key characteristics of the emerging process design. Based on the case study findings, we propose a heuristic model of an innovation process for manufacturing innovation. The proposed model uses an iterative process that allows experimentation and exploration with manufacturing innovation. The iterative approach continuously enhances knowledge levels and incorporates this knowledge in the process to refine the design of the manufacturing innovation. Furthermore, the iterative process design supports partitioning the complexity of the manufacturing innovation into smaller parts, which are easier to grasp, thereby improving the conditions for the successful adoption of manufacturing innovations for Industry 4.0.
{"title":"Manufacturing Innovation: A Heuristic Model of Innovation Processes for Industry 4.0","authors":"Maria Stoettrup Schioenning Larsen, Astrid Heidemann Lassen, Casper Schou","doi":"10.3390/asi6060098","DOIUrl":"https://doi.org/10.3390/asi6060098","url":null,"abstract":"Despite the promising potential of Industry 4.0, the transition of the manufacturing industry is still very slow-paced. In this article, we argue that one reason for this development is the fact that existing foundational process models of manufacturing innovation are developed for steady-state conditions, not considering the complexity and uncertainty related to Industry 4.0. This lack of models built for the characteristics of Industry 4.0 further translates into a lack of operational approaches and insights into engaging with Industry 4.0 in practice. Therefore, this article presents a case study of developing a comprehensive Industry 4.0 solution and identifies key characteristics of the emerging process design. Based on the case study findings, we propose a heuristic model of an innovation process for manufacturing innovation. The proposed model uses an iterative process that allows experimentation and exploration with manufacturing innovation. The iterative approach continuously enhances knowledge levels and incorporates this knowledge in the process to refine the design of the manufacturing innovation. Furthermore, the iterative process design supports partitioning the complexity of the manufacturing innovation into smaller parts, which are easier to grasp, thereby improving the conditions for the successful adoption of manufacturing innovations for Industry 4.0.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"20 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135169401","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}
Left ventricular assist devices (LVADs) technology requires developing and implementing intelligent control systems to optimize pump speed to achieve physiological metabolic demands for heart failure (HF) patients. This work aimed to design an advanced tracking control algorithm to drive an LVAD under different physiological conditions. The pole placement method, in conjunction with the sliding mode control approach (PP-SMC), was utilized to construct the proposed control method. In this design, the method was adopted to use neural networks to eliminate system uncertainties of disturbances. An elastance function was also developed and used as an input signal to mimic the physiological perfusion of HF patients. Two scenarios, ranging from rest to exercise, were introduced to evaluate the proposed technique. This technique used a lumped parameter model of the cardiovascular system (CVS) for this evaluation. The results demonstrated that the designed controller was robustly tracking the input signal in the presence of the system parameter variations of CVS. In both scenarios, the proposed method shows that the controller automatically drives the LVAD with a minimum flow of 1.7 L/min to prevent suction and 5.7 L/min to prevent over-perfusion.
{"title":"An Advanced Physiological Control Algorithm for Left Ventricular Assist Devices","authors":"Mohsen Bakouri","doi":"10.3390/asi6060097","DOIUrl":"https://doi.org/10.3390/asi6060097","url":null,"abstract":"Left ventricular assist devices (LVADs) technology requires developing and implementing intelligent control systems to optimize pump speed to achieve physiological metabolic demands for heart failure (HF) patients. This work aimed to design an advanced tracking control algorithm to drive an LVAD under different physiological conditions. The pole placement method, in conjunction with the sliding mode control approach (PP-SMC), was utilized to construct the proposed control method. In this design, the method was adopted to use neural networks to eliminate system uncertainties of disturbances. An elastance function was also developed and used as an input signal to mimic the physiological perfusion of HF patients. Two scenarios, ranging from rest to exercise, were introduced to evaluate the proposed technique. This technique used a lumped parameter model of the cardiovascular system (CVS) for this evaluation. The results demonstrated that the designed controller was robustly tracking the input signal in the presence of the system parameter variations of CVS. In both scenarios, the proposed method shows that the controller automatically drives the LVAD with a minimum flow of 1.7 L/min to prevent suction and 5.7 L/min to prevent over-perfusion.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"66 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315888","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}
The field of health and medical sciences has witnessed a surge of published research exploring the applications of ChatGPT. However, there remains a dearth of knowledge regarding its specific potential and limitations within the domain of nutrition. Given the increasing prevalence of nutrition-related diseases, there is a critical need to prioritize the promotion of a comprehensive understanding of nutrition. This paper examines the potential utility of ChatGPT as a tool for improving nutrition knowledge. Specifically, it scrutinizes its characteristics in relation to personalized meal planning, dietary advice and guidance, food intake tracking, educational materials, and other commonly found features in nutrition applications. Additionally, it explores the potential of ChatGPT to support each stage of the Nutrition Care Process. Addressing the prevailing question of whether ChatGPT can replace healthcare professionals, this paper elucidates its substantial limitations within the context of nutrition practice and education. These limitations encompass factors such as incorrect responses, coordinated nutrition services, hands-on demonstration, physical examination, verbal and non-verbal cues, emotional and psychological aspects, real-time monitoring and feedback, wearable device integration, and ethical and privacy concerns have been highlighted. In summary, ChatGPT holds promise as a valuable tool for enhancing nutrition knowledge, but further research and development are needed to optimize its capabilities in this domain.
{"title":"ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge","authors":"Manuel B. Garcia","doi":"10.3390/asi6050096","DOIUrl":"https://doi.org/10.3390/asi6050096","url":null,"abstract":"The field of health and medical sciences has witnessed a surge of published research exploring the applications of ChatGPT. However, there remains a dearth of knowledge regarding its specific potential and limitations within the domain of nutrition. Given the increasing prevalence of nutrition-related diseases, there is a critical need to prioritize the promotion of a comprehensive understanding of nutrition. This paper examines the potential utility of ChatGPT as a tool for improving nutrition knowledge. Specifically, it scrutinizes its characteristics in relation to personalized meal planning, dietary advice and guidance, food intake tracking, educational materials, and other commonly found features in nutrition applications. Additionally, it explores the potential of ChatGPT to support each stage of the Nutrition Care Process. Addressing the prevailing question of whether ChatGPT can replace healthcare professionals, this paper elucidates its substantial limitations within the context of nutrition practice and education. These limitations encompass factors such as incorrect responses, coordinated nutrition services, hands-on demonstration, physical examination, verbal and non-verbal cues, emotional and psychological aspects, real-time monitoring and feedback, wearable device integration, and ethical and privacy concerns have been highlighted. In summary, ChatGPT holds promise as a valuable tool for enhancing nutrition knowledge, but further research and development are needed to optimize its capabilities in this domain.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"2001 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135413244","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}
Mohammad Mahbubur Rahman Khan Mamun, Tarek Elfouly
Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis or monitoring are based on expert knowledge and rule-centered algorithms. In recent years, with the advancement of artificial intelligence, more and more researchers are using deep learning (ML) and deep learning (DL) with ECG data to detect different types of cardiac issues as well as other health problems such as respiration rate, sleep apnea, and blood pressure, etc. This study presents an extensive literature review based on research performed in the last few years where ML and DL have been applied with ECG data for many diagnoses. However, the review found that, in published work, the results showed promise. However, some significant limitations kept that technique from implementation in reality and being used for medical decisions; examples of such limitations are imbalanced and the absence of standardized dataset for evaluation, lack of interpretability of the model, inconsistency of performance while using a new dataset, security, and privacy of health data and lack of collaboration with physicians, etc. AI using ECG data accompanied by modern wearable biosensor technologies has the potential to allow for health monitoring and early diagnosis within reach of larger populations. However, researchers should focus on resolving the limitations.
{"title":"AI-Enabled Electrocardiogram Analysis for Disease Diagnosis","authors":"Mohammad Mahbubur Rahman Khan Mamun, Tarek Elfouly","doi":"10.3390/asi6050095","DOIUrl":"https://doi.org/10.3390/asi6050095","url":null,"abstract":"Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis or monitoring are based on expert knowledge and rule-centered algorithms. In recent years, with the advancement of artificial intelligence, more and more researchers are using deep learning (ML) and deep learning (DL) with ECG data to detect different types of cardiac issues as well as other health problems such as respiration rate, sleep apnea, and blood pressure, etc. This study presents an extensive literature review based on research performed in the last few years where ML and DL have been applied with ECG data for many diagnoses. However, the review found that, in published work, the results showed promise. However, some significant limitations kept that technique from implementation in reality and being used for medical decisions; examples of such limitations are imbalanced and the absence of standardized dataset for evaluation, lack of interpretability of the model, inconsistency of performance while using a new dataset, security, and privacy of health data and lack of collaboration with physicians, etc. AI using ECG data accompanied by modern wearable biosensor technologies has the potential to allow for health monitoring and early diagnosis within reach of larger populations. However, researchers should focus on resolving the limitations.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135617543","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}
Gun violence and mass shootings kill and injure people, create psychological trauma, damage properties, and cause economic loss. The loss from gun violence can be reduced if we can detect the gunshot early and notify the police as soon as possible. In this project, a novel gunshot detector device is developed that automatically detects indoor gunshot sound and sends the gunshot location to the nearby police station in real time using the Internet. The users of the device and the emergency responders also receive smartphone notifications whenever the shooting happens. This will help the emergency responders to quickly arrive at the crime scene, thus the shooter can be caught, injured people can be taken to the hospital quickly, and lives can be saved. The gunshot detector is an electronic device that can be placed in schools, shopping malls, offices, etc. The device also records the gunshot sounds for post-crime scene analysis. A deep learning model, based on a convolutional neural network (CNN), is trained to classify the gunshot sound from other sounds with 98% accuracy. A prototype of the gunshot detector device, the central server for the emergency responder’s station, and smartphone apps have been developed and tested successfully.
{"title":"Towards an Indoor Gunshot Detection and Notification System Using Deep Learning","authors":"Tareq Khan","doi":"10.3390/asi6050094","DOIUrl":"https://doi.org/10.3390/asi6050094","url":null,"abstract":"Gun violence and mass shootings kill and injure people, create psychological trauma, damage properties, and cause economic loss. The loss from gun violence can be reduced if we can detect the gunshot early and notify the police as soon as possible. In this project, a novel gunshot detector device is developed that automatically detects indoor gunshot sound and sends the gunshot location to the nearby police station in real time using the Internet. The users of the device and the emergency responders also receive smartphone notifications whenever the shooting happens. This will help the emergency responders to quickly arrive at the crime scene, thus the shooter can be caught, injured people can be taken to the hospital quickly, and lives can be saved. The gunshot detector is an electronic device that can be placed in schools, shopping malls, offices, etc. The device also records the gunshot sounds for post-crime scene analysis. A deep learning model, based on a convolutional neural network (CNN), is trained to classify the gunshot sound from other sounds with 98% accuracy. A prototype of the gunshot detector device, the central server for the emergency responder’s station, and smartphone apps have been developed and tested successfully.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135779430","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}
Yazeed S. Jweihan, Mazen J. Al-Kheetan, Musab Rabi
Moisture susceptibility is a complex phenomenon that induces various distresses in asphalt pavements and can be assessed by the Retained Stability Index (RSI). This study proposes a robust model to predict the RSI using a hybrid machine learning technique, including Artificial Neural Network (ANN) and Gene Expression Programming. The model is expressed as a simple and direct mathematical function with input variables of mineral filler proportion (F%), water absorption rate of combined aggregate (Ab%), asphalt content (AC%), and air void content (Va%). A relative importance analysis ranked AC% as the most influential variable on RSI, followed by Va%, F%, and Ab%. The experimental RSI results of 150 testing samples of various mixes were utilized along with other data points generated by the ANN to train and validate the proposed model. The model promotes a high level of accuracy for predicting the RSI with a 96.6% coefficient of determination (R2) and very low errors. In addition, the sensitivity of the model has been verified by considering the effect of the variables, which is in line with the results of network connection weight and previous studies in the literature. F%, Ab%, and Va% have an inverse relationship with the RSI values, whereas AC% has the opposite. The model helps forecast the water susceptibility of asphalt mixes by which the experimental effort is minimized and the mixes’ performance can be improved.
{"title":"Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach","authors":"Yazeed S. Jweihan, Mazen J. Al-Kheetan, Musab Rabi","doi":"10.3390/asi6050093","DOIUrl":"https://doi.org/10.3390/asi6050093","url":null,"abstract":"Moisture susceptibility is a complex phenomenon that induces various distresses in asphalt pavements and can be assessed by the Retained Stability Index (RSI). This study proposes a robust model to predict the RSI using a hybrid machine learning technique, including Artificial Neural Network (ANN) and Gene Expression Programming. The model is expressed as a simple and direct mathematical function with input variables of mineral filler proportion (F%), water absorption rate of combined aggregate (Ab%), asphalt content (AC%), and air void content (Va%). A relative importance analysis ranked AC% as the most influential variable on RSI, followed by Va%, F%, and Ab%. The experimental RSI results of 150 testing samples of various mixes were utilized along with other data points generated by the ANN to train and validate the proposed model. The model promotes a high level of accuracy for predicting the RSI with a 96.6% coefficient of determination (R2) and very low errors. In addition, the sensitivity of the model has been verified by considering the effect of the variables, which is in line with the results of network connection weight and previous studies in the literature. F%, Ab%, and Va% have an inverse relationship with the RSI values, whereas AC% has the opposite. The model helps forecast the water susceptibility of asphalt mixes by which the experimental effort is minimized and the mixes’ performance can be improved.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883115","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}
This paper presents multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between 25 May 2020 and 31 January 2023. First, the analysis shows that the average number of Tweets per month wherein individuals self-reported Long COVID on Twitter was considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings from sentiment analysis using VADER show that the percentages of Tweets with positive, negative, and neutral sentiments were 43.1%, 42.7%, and 14.2%, respectively. To add to this, most of the Tweets with a positive sentiment, as well as most of the Tweets with a negative sentiment, were not highly polarized. Third, the result of tokenization indicates that the tweeting patterns (in terms of the number of tokens used) were similar for the positive and negative Tweets. Analysis of these results also shows that there was no direct relationship between the number of tokens used and the intensity of the sentiment expressed in these Tweets. Finally, a granular analysis of the sentiments showed that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.
{"title":"Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis","authors":"Nirmalya Thakur","doi":"10.3390/asi6050092","DOIUrl":"https://doi.org/10.3390/asi6050092","url":null,"abstract":"This paper presents multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between 25 May 2020 and 31 January 2023. First, the analysis shows that the average number of Tweets per month wherein individuals self-reported Long COVID on Twitter was considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings from sentiment analysis using VADER show that the percentages of Tweets with positive, negative, and neutral sentiments were 43.1%, 42.7%, and 14.2%, respectively. To add to this, most of the Tweets with a positive sentiment, as well as most of the Tweets with a negative sentiment, were not highly polarized. Third, the result of tokenization indicates that the tweeting patterns (in terms of the number of tokens used) were similar for the positive and negative Tweets. Analysis of these results also shows that there was no direct relationship between the number of tokens used and the intensity of the sentiment expressed in these Tweets. Finally, a granular analysis of the sentiments showed that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136012520","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}
Ghazanfar Latif, Ghassen Ben Brahim, Sherif E. Abdelhamid, Runna Alghazo, Ghadah Alhabib, Khalid Alnujaidi
Visual impairment should not hinder an individual from achieving their aspirations, nor should it be a hindrance to their contributions to society. The age in which persons with disabilities were treated unfairly is long gone, and individuals with disabilities are productive members of society nowadays, especially when they receive the right education and are given the right tools to succeed. Thus, it is imperative to integrate the latest technologies into devices and software that could assist persons with disabilities. The Internet of Things (IoT), artificial intelligence (AI), and Deep Learning (ML)/deep learning (DL) are technologies that have gained momentum over the past decade and could be integrated to assist persons with disabilities—visually impaired individuals. In this paper, we propose an IoT-based system that can fit on the ring finger and can simulate the real-life experience of a visually impaired person. The system can learn and translate Arabic and English braille into audio using deep learning techniques enhanced with transfer learning. The system is developed to assist both visually impaired individuals and their family members in learning braille through the use of the ring-based device, which captures a braille image using an embedded camera, recognizes it, and translates it into audio. The recognition of the captured braille image is achieved through a transfer learning-based Convolutional Neural Network (CNN).
{"title":"Learning at Your Fingertips: An Innovative IoT-Based AI-Powered Braille Learning System","authors":"Ghazanfar Latif, Ghassen Ben Brahim, Sherif E. Abdelhamid, Runna Alghazo, Ghadah Alhabib, Khalid Alnujaidi","doi":"10.3390/asi6050091","DOIUrl":"https://doi.org/10.3390/asi6050091","url":null,"abstract":"Visual impairment should not hinder an individual from achieving their aspirations, nor should it be a hindrance to their contributions to society. The age in which persons with disabilities were treated unfairly is long gone, and individuals with disabilities are productive members of society nowadays, especially when they receive the right education and are given the right tools to succeed. Thus, it is imperative to integrate the latest technologies into devices and software that could assist persons with disabilities. The Internet of Things (IoT), artificial intelligence (AI), and Deep Learning (ML)/deep learning (DL) are technologies that have gained momentum over the past decade and could be integrated to assist persons with disabilities—visually impaired individuals. In this paper, we propose an IoT-based system that can fit on the ring finger and can simulate the real-life experience of a visually impaired person. The system can learn and translate Arabic and English braille into audio using deep learning techniques enhanced with transfer learning. The system is developed to assist both visually impaired individuals and their family members in learning braille through the use of the ring-based device, which captures a braille image using an embedded camera, recognizes it, and translates it into audio. The recognition of the captured braille image is achieved through a transfer learning-based Convolutional Neural Network (CNN).","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136211407","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}
This article addresses the need for early emergency detection and safety monitoring in public spaces using deep learning techniques. The problem of discerning relevant sound events in urban environments is identified, which is essential to respond quickly to possible incidents. To solve this, a method is proposed based on extracting acoustic features from captured audio signals and using a deep learning model trained with data collected both from the environment and from specialized libraries. The results show performance metrics such as precision, completeness, F1-score, and ROC-AUC curve and discuss detailed confusion matrices and false positive and negative analysis. Comparing this approach with related works highlights its effectiveness and potential in detecting sound events. The article identifies areas for future research, including incorporating real-world data and exploring more advanced neural architectures, and reaffirms the importance of deep learning in public safety.
{"title":"Application of Deep Learning in the Early Detection of Emergency Situations and Security Monitoring in Public Spaces","authors":"William Villegas-Ch, Jaime Govea","doi":"10.3390/asi6050090","DOIUrl":"https://doi.org/10.3390/asi6050090","url":null,"abstract":"This article addresses the need for early emergency detection and safety monitoring in public spaces using deep learning techniques. The problem of discerning relevant sound events in urban environments is identified, which is essential to respond quickly to possible incidents. To solve this, a method is proposed based on extracting acoustic features from captured audio signals and using a deep learning model trained with data collected both from the environment and from specialized libraries. The results show performance metrics such as precision, completeness, F1-score, and ROC-AUC curve and discuss detailed confusion matrices and false positive and negative analysis. Comparing this approach with related works highlights its effectiveness and potential in detecting sound events. The article identifies areas for future research, including incorporating real-world data and exploring more advanced neural architectures, and reaffirms the importance of deep learning in public safety.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135251437","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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