Pub Date : 2023-06-08DOI: 10.1109/HORA58378.2023.10156800
E. Solly, Ahmed Aldabbagh
In this paper, a 3D printed remote-gesture-controlled maneuverable robot is presented. The manipulator system is based on two hand-worn glove controllers, represented by robotic manipulator arm and the robot vehicle. The developed approach of Human-Computer Interaction (HCI) employs the Arduino- platform, where both hands are used simultaneously to control the robot, expanding the human-robot interaction to a more humanly accessible design that can remotely control both an industrial style 5-axis robotic manipulator and a robot vehicle using hand gestures. The results prove the viability of using glove controllers for this developed design and the possibility of industrial implementations, such as gesture-controlled robotics that can benefit from further advancement. Moreover, the presented dynamic gesture control methods are a crucial medium for human-robot interaction (HRI).
{"title":"Gesture Controlled Mobile Robot","authors":"E. Solly, Ahmed Aldabbagh","doi":"10.1109/HORA58378.2023.10156800","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156800","url":null,"abstract":"In this paper, a 3D printed remote-gesture-controlled maneuverable robot is presented. The manipulator system is based on two hand-worn glove controllers, represented by robotic manipulator arm and the robot vehicle. The developed approach of Human-Computer Interaction (HCI) employs the Arduino- platform, where both hands are used simultaneously to control the robot, expanding the human-robot interaction to a more humanly accessible design that can remotely control both an industrial style 5-axis robotic manipulator and a robot vehicle using hand gestures. The results prove the viability of using glove controllers for this developed design and the possibility of industrial implementations, such as gesture-controlled robotics that can benefit from further advancement. Moreover, the presented dynamic gesture control methods are a crucial medium for human-robot interaction (HRI).","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115766362","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10155771
I. Sierova, I. Aksonova, V. Shlykova, Tetiana Milevska
Based on the integration orientation of the development of the national economy, as the direction of its growth and competitiveness, general approaches to its analytical assessment are defined. The analysis of favorable conditions of integration is combined with the correctness of the implementation of analytical generalizations as a basis for the formation of legitimate conclusions. Based on the fact that the determination of real trends reflects the relative characteristics of the dynamics, a comparative analysis was conducted, which confirmed the relative stability of the export potential of the Ukrainian grain crops market. The calculation of the basic indicators of the economic openness by grain crops in comparison with the general level for the country indicated the similarity of trends, but a higher level of stability. The general conclusion regarding the impact of grain exports on the level of the country's competitiveness is confirmed by a comparative analysis of the Global competitiveness index trends and the economic openness of the Ukrainian grain market.
{"title":"Computer-mathematical support for analytical assessment of trends in the Ukrainian grain market development","authors":"I. Sierova, I. Aksonova, V. Shlykova, Tetiana Milevska","doi":"10.1109/HORA58378.2023.10155771","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10155771","url":null,"abstract":"Based on the integration orientation of the development of the national economy, as the direction of its growth and competitiveness, general approaches to its analytical assessment are defined. The analysis of favorable conditions of integration is combined with the correctness of the implementation of analytical generalizations as a basis for the formation of legitimate conclusions. Based on the fact that the determination of real trends reflects the relative characteristics of the dynamics, a comparative analysis was conducted, which confirmed the relative stability of the export potential of the Ukrainian grain crops market. The calculation of the basic indicators of the economic openness by grain crops in comparison with the general level for the country indicated the similarity of trends, but a higher level of stability. The general conclusion regarding the impact of grain exports on the level of the country's competitiveness is confirmed by a comparative analysis of the Global competitiveness index trends and the economic openness of the Ukrainian grain market.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123874840","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156738
Rian Sh. Al-Yozbaky, M. Alanezi
The most common detrimental technique used by attackers to deceive victims into disclosing personal information is phishing, in which they pose as trustworthy individuals or organizations often via email. Although fake email attacks are a common tactic used by cybercriminals, their use has recently increased as attacker's profit from victims' anxiety. As a result, further study is required to determine how to recognize bogus emails. This paper proposed a new model to extract the Arabic email content and compare it using three determinants based on neural language programming (NLP) for the purpose of discovering whether it is a legitimate email or a phishing email. The first is a black list of Arabic common phishing words, the roots of a black list of Arabic common phishing words, and a list of Arabic common phishing sentences, the best two results for applying the above conditions were (99% Legal and 96% Phishing) when using the three conditions together and (99% Legal and 94% Phishing) when using a blacklist of common words of phishing, and then will present and discuss the results obtained.
{"title":"Detection and Analyzing Phishing Emails Using NLP Techniques","authors":"Rian Sh. Al-Yozbaky, M. Alanezi","doi":"10.1109/HORA58378.2023.10156738","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156738","url":null,"abstract":"The most common detrimental technique used by attackers to deceive victims into disclosing personal information is phishing, in which they pose as trustworthy individuals or organizations often via email. Although fake email attacks are a common tactic used by cybercriminals, their use has recently increased as attacker's profit from victims' anxiety. As a result, further study is required to determine how to recognize bogus emails. This paper proposed a new model to extract the Arabic email content and compare it using three determinants based on neural language programming (NLP) for the purpose of discovering whether it is a legitimate email or a phishing email. The first is a black list of Arabic common phishing words, the roots of a black list of Arabic common phishing words, and a list of Arabic common phishing sentences, the best two results for applying the above conditions were (99% Legal and 96% Phishing) when using the three conditions together and (99% Legal and 94% Phishing) when using a blacklist of common words of phishing, and then will present and discuss the results obtained.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124302026","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10155779
Baidaa Y. Mohemed, Sura. Alasdi, Sokina Fakfry, Khalid Haneen Abass, Ashraq Mohammed Kadim
In this study, several optical and structural properties of polymers were investigated to determine how antimony trioxide Sb2O3 addition affected them. Several films have been produced for this purpose by adding Sb2O3 to polymers at varying concentrations using the casting procedure. Between 200 and 1100 nm, the transmittance and absorbance spectra were analyzed. All of the properties of the indirect allowed transition have been calculated, including its absorption, extinction, index of refraction, real and imaginary dielectric constants, and energy gap. Since the absorption coefficient was less than 104 cm-1, the experimental results indicated that electronic transitions could occur indirectly. Increasing the Sb2O3 concentration increased the index of refraction and extinction coefficient. Similar energy gaps were predicted by the Tauc and Wemple-DiDomenico models.
{"title":"Dispersion Parameters of PVA-PAAm-Sb2O3 Nanocomposites Prepared by Casting Solution Method","authors":"Baidaa Y. Mohemed, Sura. Alasdi, Sokina Fakfry, Khalid Haneen Abass, Ashraq Mohammed Kadim","doi":"10.1109/HORA58378.2023.10155779","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10155779","url":null,"abstract":"In this study, several optical and structural properties of polymers were investigated to determine how antimony trioxide Sb2O3 addition affected them. Several films have been produced for this purpose by adding Sb2O3 to polymers at varying concentrations using the casting procedure. Between 200 and 1100 nm, the transmittance and absorbance spectra were analyzed. All of the properties of the indirect allowed transition have been calculated, including its absorption, extinction, index of refraction, real and imaginary dielectric constants, and energy gap. Since the absorption coefficient was less than 104 cm-1, the experimental results indicated that electronic transitions could occur indirectly. Increasing the Sb2O3 concentration increased the index of refraction and extinction coefficient. Similar energy gaps were predicted by the Tauc and Wemple-DiDomenico models.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126050015","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156689
Samiha Fairooz, Shakila Yeasmin Miti, Zihadul Islam, Meem Tasfia Zaman
An authoritative healthcare system guarantees access to high-quality medical care to the population to boost their comprehensive health condition in the most effective means. The Blockchain technology is anticipated to make animminent change in the entire healthcare entity by securing the electronic medical records. The paper intends to put emphasison a web application especially, a medical history card with a vision of overcoming the deficiencies of the existing healthcare system of Bangladesh. Notwithstanding that, this researchlikewise delineates the ability of the decentralized database of the Blockchain technology to improvise the respective system. In particular, the aforementioned card highlights a patient's particulars, diagnoses, prescriptions, vaccinations, drug history, investigation profile, family history, blood transfusion history, and many more since birth. These records are the key to having a successful treatment whatsoever. With this in mind, the history card would securely upgrade the current healthcare world.
{"title":"A medical history card utilizing the Blockchain technology","authors":"Samiha Fairooz, Shakila Yeasmin Miti, Zihadul Islam, Meem Tasfia Zaman","doi":"10.1109/HORA58378.2023.10156689","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156689","url":null,"abstract":"An authoritative healthcare system guarantees access to high-quality medical care to the population to boost their comprehensive health condition in the most effective means. The Blockchain technology is anticipated to make animminent change in the entire healthcare entity by securing the electronic medical records. The paper intends to put emphasison a web application especially, a medical history card with a vision of overcoming the deficiencies of the existing healthcare system of Bangladesh. Notwithstanding that, this researchlikewise delineates the ability of the decentralized database of the Blockchain technology to improvise the respective system. In particular, the aforementioned card highlights a patient's particulars, diagnoses, prescriptions, vaccinations, drug history, investigation profile, family history, blood transfusion history, and many more since birth. These records are the key to having a successful treatment whatsoever. With this in mind, the history card would securely upgrade the current healthcare world.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126054506","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156702
O. Laptiev, A. Musienko, Volodymyr Nakonechnyi, A. Sobchuk, S. Gakhov, Serhii Kopytko
Abnormalities in network traffic can be caused by malfunctioning network equipment, accidental or intentional actions by users, or the actions of attackers. Thus, for reliable data transmission in the information network, it is necessary to take measures to detect anomalies in a timely manner and take measures to eliminate them. Therefore, in order to ensure reliable data transmission in the network, the development of new methods for detecting anomalies is of urgent importance. This work is devoted to the development of an improved algorithm for recognizing network traffic anomalies based on artificial intelligence. On the basis of the conducted analysis and research, an improved algorithm was developed for the most accurate determination of an abnormal state. The principle component analysis algorithm was taken as a basis and a type of Generative adversarial network algorithm, a machine learning algorithm without a teacher, was added to it, namely BIGAN, which uses an encoder in its activity, namely, thanks to its E encoder, it is able to detect anomalies in input and processed data, which made it possible to detect network traffic anomalies with greater accuracy and in less time.
{"title":"Algorithm for Recognition of Network Traffic Anomalies Based on Artificial Intelligence","authors":"O. Laptiev, A. Musienko, Volodymyr Nakonechnyi, A. Sobchuk, S. Gakhov, Serhii Kopytko","doi":"10.1109/HORA58378.2023.10156702","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156702","url":null,"abstract":"Abnormalities in network traffic can be caused by malfunctioning network equipment, accidental or intentional actions by users, or the actions of attackers. Thus, for reliable data transmission in the information network, it is necessary to take measures to detect anomalies in a timely manner and take measures to eliminate them. Therefore, in order to ensure reliable data transmission in the network, the development of new methods for detecting anomalies is of urgent importance. This work is devoted to the development of an improved algorithm for recognizing network traffic anomalies based on artificial intelligence. On the basis of the conducted analysis and research, an improved algorithm was developed for the most accurate determination of an abnormal state. The principle component analysis algorithm was taken as a basis and a type of Generative adversarial network algorithm, a machine learning algorithm without a teacher, was added to it, namely BIGAN, which uses an encoder in its activity, namely, thanks to its E encoder, it is able to detect anomalies in input and processed data, which made it possible to detect network traffic anomalies with greater accuracy and in less time.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124884899","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10155772
I. Javed, H. Afzal
Social media platforms have become the go-to medium for connecting people in this era of the internet. Twitter has emerged as a popular platform that allowsusers to share their views on current events and political organizations, providing a wealth of political information. The aim of this study is to utilize natural language processing techniques to analyze a dataset extracted from Twitter. This involves retrieving data from Twitter, performing sentiment analysis using deeplearning approaches, and creating a Python library that classifiesinput texts as either positive or negative. The training data used in this study included the Roman-Urdu language, comprising 89793 entries. Various classification models were employed to categorize emotions, with the ensemble technique ultimately used to determine the results. The LSTM classifier achieved an accuracy of 87%, while the Bert model performed the best with 90% accuracy.
{"title":"Opinion Analysis of Bi-Lingual Event Data from Social Networks","authors":"I. Javed, H. Afzal","doi":"10.1109/HORA58378.2023.10155772","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10155772","url":null,"abstract":"Social media platforms have become the go-to medium for connecting people in this era of the internet. Twitter has emerged as a popular platform that allowsusers to share their views on current events and political organizations, providing a wealth of political information. The aim of this study is to utilize natural language processing techniques to analyze a dataset extracted from Twitter. This involves retrieving data from Twitter, performing sentiment analysis using deeplearning approaches, and creating a Python library that classifiesinput texts as either positive or negative. The training data used in this study included the Roman-Urdu language, comprising 89793 entries. Various classification models were employed to categorize emotions, with the ensemble technique ultimately used to determine the results. The LSTM classifier achieved an accuracy of 87%, while the Bert model performed the best with 90% accuracy.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131377053","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156805
Yagmur Selenay Selcuk, Elvin Çoban
Providing healthcare services at home is crucial for patients who require long-term care or face mobility or other health-related constraints that prevent them from traveling to healthcare facilities. Effective data analysis techniques are needed to optimize these services to understand patient needs and allocate resources efficiently. Machine learning algorithms can analyze big datasets generated from home healthcare services to identify patterns, trends, and predictive factors. By utilizing these techniques, predictive models for service time can be developed, leading to improved patient outcomes, increased efficiency, and reduced costs. This study explores the significance of various features in predicting service time for home healthcare services by analyzing real-life data using data analysis techniques. By developing a correlation matrix, healthcare providers can examine the relationships between features as well as their connections with the target value, thereby providing valuable managerial insights into improving the quality of home healthcare services through enhanced predictions of service time.
{"title":"Advancing Home Healthcare Through Machine Learning: Predicting Service Time for Enhanced Patient Care","authors":"Yagmur Selenay Selcuk, Elvin Çoban","doi":"10.1109/HORA58378.2023.10156805","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156805","url":null,"abstract":"Providing healthcare services at home is crucial for patients who require long-term care or face mobility or other health-related constraints that prevent them from traveling to healthcare facilities. Effective data analysis techniques are needed to optimize these services to understand patient needs and allocate resources efficiently. Machine learning algorithms can analyze big datasets generated from home healthcare services to identify patterns, trends, and predictive factors. By utilizing these techniques, predictive models for service time can be developed, leading to improved patient outcomes, increased efficiency, and reduced costs. This study explores the significance of various features in predicting service time for home healthcare services by analyzing real-life data using data analysis techniques. By developing a correlation matrix, healthcare providers can examine the relationships between features as well as their connections with the target value, thereby providing valuable managerial insights into improving the quality of home healthcare services through enhanced predictions of service time.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123783234","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156733
Aws Khudhur, N. Ramaha
Predicting student performance is a crucial area of research in the field of education. To improve the accuracy and reliability of student performance prediction, machine learning (ML) techniques have been widely used. In this study, we propose a novel approach for predicting student performance using five ML techniques, which include data analysis, pre-processing techniques, and data augmentation using GAN. We evaluate the proposed approach using a real-world dataset of student academic records and compare the results to those obtained without data augmentation. Our findings demonstrate that data augmentation significantly improves the accuracy and reliability of student performance prediction. Specifically, the random forest classifier achieves the best accuracy of 99.8%. This research contributes to the field of education by providing a more comprehensive and accurate model for predicting student performance, which can support informed decision-making and improve educational outcomes.
{"title":"Students' Performance Prediction Using Machine Learning Based on Generative Adversarial Network","authors":"Aws Khudhur, N. Ramaha","doi":"10.1109/HORA58378.2023.10156733","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156733","url":null,"abstract":"Predicting student performance is a crucial area of research in the field of education. To improve the accuracy and reliability of student performance prediction, machine learning (ML) techniques have been widely used. In this study, we propose a novel approach for predicting student performance using five ML techniques, which include data analysis, pre-processing techniques, and data augmentation using GAN. We evaluate the proposed approach using a real-world dataset of student academic records and compare the results to those obtained without data augmentation. Our findings demonstrate that data augmentation significantly improves the accuracy and reliability of student performance prediction. Specifically, the random forest classifier achieves the best accuracy of 99.8%. This research contributes to the field of education by providing a more comprehensive and accurate model for predicting student performance, which can support informed decision-making and improve educational outcomes.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115868649","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10155786
Aniebiet Micheal Ezekiel, R. Obermaisser
Recent research on Artificial Neural Networks (ANNs) has shown significant improvement in machine learning over traditional algorithms in many disciplines. This paper contributes to the advances in medical science and AI technologies by exploring this promising technology for real-time cardiovascular complication detection and resuscitation during rescue missions. Previous studies have relied on cloud-based computing or specialized hardware such as graphics processing units (GPUs), which can be expensive and require significant power consumption. Additionally, existing AI models are often not optimized for low-latency processing, hindering their real-time applications. This study proposes a PyTorch-based ANN model with time optimization techniques on the field-programmable gate arrays (FPGAs) hardware platform, providing data privacy and hardware security. Our approach includes intermediate layer saving and layer parameter reuse, reducing computational complexity and memory requirements while maintaining accuracy. The prototype wearable utilizes a Trenz Electronics TE0802 FPGA board with custom PYNQ-Linux software, providing a low-cost, low-power, and high-performance hardware platform. Using the Apache TVM toolchain, our ANN model predicts cardiovascular disease risk and aids rescuers in making rapid and precise clinical decisions. The results demonstrate 95.9% accuracy in detecting cardiovascular complications, with an average execution time of 41.99ms using TVM. Additionally, our time optimization technique achieves reduced inference times of 33%, 55%, and 79% for reusing the saved output files of layers 1, 2, and 3, respectively, as validated through simulations and experiments.
{"title":"Time-Optimized Detection of Cardiovascular Complications with Artificial Intelligence in Rescue Operations using FPGA-based Wearable","authors":"Aniebiet Micheal Ezekiel, R. Obermaisser","doi":"10.1109/HORA58378.2023.10155786","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10155786","url":null,"abstract":"Recent research on Artificial Neural Networks (ANNs) has shown significant improvement in machine learning over traditional algorithms in many disciplines. This paper contributes to the advances in medical science and AI technologies by exploring this promising technology for real-time cardiovascular complication detection and resuscitation during rescue missions. Previous studies have relied on cloud-based computing or specialized hardware such as graphics processing units (GPUs), which can be expensive and require significant power consumption. Additionally, existing AI models are often not optimized for low-latency processing, hindering their real-time applications. This study proposes a PyTorch-based ANN model with time optimization techniques on the field-programmable gate arrays (FPGAs) hardware platform, providing data privacy and hardware security. Our approach includes intermediate layer saving and layer parameter reuse, reducing computational complexity and memory requirements while maintaining accuracy. The prototype wearable utilizes a Trenz Electronics TE0802 FPGA board with custom PYNQ-Linux software, providing a low-cost, low-power, and high-performance hardware platform. Using the Apache TVM toolchain, our ANN model predicts cardiovascular disease risk and aids rescuers in making rapid and precise clinical decisions. The results demonstrate 95.9% accuracy in detecting cardiovascular complications, with an average execution time of 41.99ms using TVM. Additionally, our time optimization technique achieves reduced inference times of 33%, 55%, and 79% for reusing the saved output files of layers 1, 2, and 3, respectively, as validated through simulations and experiments.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121361162","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：