MalScanner is a tool that aims to provide a simple, effective, and user-friendly method of scanning files for malicious behavior. Furthermore, MalScanner scans a file and extracts features to be used in machine learning assisted static malware analysis and inspects the file’s behavior dynamically. This tool also implements a blockchain database to store analysis results. The solution will be presented to the user in a straightforward manner via web application.
{"title":"Malscanner – File Behavior Analysis using Machine Learning","authors":"Basil Abdulrahman, Abdulrahman Qanadeely, Abdulaziz Al-Hassan, Omar Al-Ghamdi, Nawaf Al-Sukaibi, N. Saqib","doi":"10.1109/LT58159.2023.10092346","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092346","url":null,"abstract":"MalScanner is a tool that aims to provide a simple, effective, and user-friendly method of scanning files for malicious behavior. Furthermore, MalScanner scans a file and extracts features to be used in machine learning assisted static malware analysis and inspects the file’s behavior dynamically. This tool also implements a blockchain database to store analysis results. The solution will be presented to the user in a straightforward manner via web application.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125049078","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 : 2023-01-26DOI: 10.1109/LT58159.2023.10092297
Norh Alshebil, Kulud Alkadi, Nagarajkumar Yenugadhati, M. Dubayee
Introduction: Diabetes therapeutic education assists patients in taking responsibility for self-management of their condition, and technology support systems promote this education. In this study, we introduce augmented reality (AR) as an instructional tool to supplement therapeutic education for diabetic patients. Objectives: The purpose of this study is to investigate two educational approaches used within the clinic: Augmented reality (AR) and traditional educational methods. The objective is to determine which one has a better impact on nutrition knowledge improvement by using the Nutritional Diabetes Knowledge Survey (NKS) score. Method: A total of 65 children and adolescent patients with Type 1 Diabetes, aged 10-16 years old, currently receiving healthcare services at the KAMC-R nutrition diabetes clinic, were invited to choose type of education for Carbohydrate counting (AR or traditional education method) and complete a diabetes nutrition knowledge survey before the education and after. The difference between scores with a higher percentage change indicate better diabetes nutrition knowledge. Results: We discovered that the children who participated in the study had an average level of knowledge regarding nutrition (a mean of 8.98 out of a score of 23). This suggests that diabetic patients require therapeutic education. When the findings of the pre-knowledge questionnaire and the post-knowledge questionnaire were compared, it was discovered that the children learned more about carbohydrate dietary choices by watching AR videos with a p-value <.0001 than by using traditional educational methods. Furthermore, the gained information was independent of gender or age. This AR instructional tool has the potential to be a significant therapeutic education tool for diabetic patients. Conclusion: This study revealed a positive impact of using a digitalized educational method (AR) on the patient’s diabetes nutrition knowledge, specifically in relation to carbohydrate counting.
{"title":"Using Augmented Reality to improve Nutritional Educational for Type 1 Diabetic Children and Adolescents: Quantitative study of Patient Knowledge Retention","authors":"Norh Alshebil, Kulud Alkadi, Nagarajkumar Yenugadhati, M. Dubayee","doi":"10.1109/LT58159.2023.10092297","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092297","url":null,"abstract":"Introduction: Diabetes therapeutic education assists patients in taking responsibility for self-management of their condition, and technology support systems promote this education. In this study, we introduce augmented reality (AR) as an instructional tool to supplement therapeutic education for diabetic patients. Objectives: The purpose of this study is to investigate two educational approaches used within the clinic: Augmented reality (AR) and traditional educational methods. The objective is to determine which one has a better impact on nutrition knowledge improvement by using the Nutritional Diabetes Knowledge Survey (NKS) score. Method: A total of 65 children and adolescent patients with Type 1 Diabetes, aged 10-16 years old, currently receiving healthcare services at the KAMC-R nutrition diabetes clinic, were invited to choose type of education for Carbohydrate counting (AR or traditional education method) and complete a diabetes nutrition knowledge survey before the education and after. The difference between scores with a higher percentage change indicate better diabetes nutrition knowledge. Results: We discovered that the children who participated in the study had an average level of knowledge regarding nutrition (a mean of 8.98 out of a score of 23). This suggests that diabetic patients require therapeutic education. When the findings of the pre-knowledge questionnaire and the post-knowledge questionnaire were compared, it was discovered that the children learned more about carbohydrate dietary choices by watching AR videos with a p-value <.0001 than by using traditional educational methods. Furthermore, the gained information was independent of gender or age. This AR instructional tool has the potential to be a significant therapeutic education tool for diabetic patients. Conclusion: This study revealed a positive impact of using a digitalized educational method (AR) on the patient’s diabetes nutrition knowledge, specifically in relation to carbohydrate counting.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130194930","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 : 2023-01-26DOI: 10.1109/LT58159.2023.10092331
C. Kumar. J, M. Arunsi. B, M. A. Majid
Covid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology.
{"title":"A Machine Learning-driven IoT Architecture for Predicting the Growth and Trend of Covid-19 Epidemic Outbreaks to Identify High-risk Locations","authors":"C. Kumar. J, M. Arunsi. B, M. A. Majid","doi":"10.1109/LT58159.2023.10092331","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092331","url":null,"abstract":"Covid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128840720","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 : 2023-01-26DOI: 10.1109/LT58159.2023.10092350
Dahlia Omran, S. Fawzi, A. Kandil
correct understanding of the Holy Quran is an essential duty for all Muslims. Tajweed rules guide the reciter to perform Holy Quran reading exactly as it was uttered by Prophet Muhammad peace be upon him. This work focused on the recognition of one Quranic recitation rule. Qalqalah rule is applied to five letters of the Arabic Alphabet (Baa/Daal/Jeem/Qaaf/Taa) having sukun vowelization. The proposed system used the Mel Frequency Cepstral Coefficients (MFCC) as the feature extraction technique, and the Convolutional Neural Networks (CNN) model was used for recognition. The available dataset consists of 3322 audio samples from different surahs of the Quran for four professional readers (Sheihk) AlHussary, AlMinshawy, Abdel Baset, and Ayman Swayed. The best results were gained using Ayman Swayed audio samples with a validation accuracy of 90.8%.
{"title":"Automatic Detection of Some Tajweed Rules","authors":"Dahlia Omran, S. Fawzi, A. Kandil","doi":"10.1109/LT58159.2023.10092350","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092350","url":null,"abstract":"correct understanding of the Holy Quran is an essential duty for all Muslims. Tajweed rules guide the reciter to perform Holy Quran reading exactly as it was uttered by Prophet Muhammad peace be upon him. This work focused on the recognition of one Quranic recitation rule. Qalqalah rule is applied to five letters of the Arabic Alphabet (Baa/Daal/Jeem/Qaaf/Taa) having sukun vowelization. The proposed system used the Mel Frequency Cepstral Coefficients (MFCC) as the feature extraction technique, and the Convolutional Neural Networks (CNN) model was used for recognition. The available dataset consists of 3322 audio samples from different surahs of the Quran for four professional readers (Sheihk) AlHussary, AlMinshawy, Abdel Baset, and Ayman Swayed. The best results were gained using Ayman Swayed audio samples with a validation accuracy of 90.8%.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":"9 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116930170","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 : 2023-01-26DOI: 10.1109/LT58159.2023.10092303
M. El-Amin
When employing pure hydrogen, its leakage poses a serious safety risk since it can cause fire or explode if it comes into contact with the air. In this study, hydrogen leakage in a form of a buoyant jet is investigated using machine learning approaches. As the experiments used to explore hydrogen leaks are extremely dangerous, and there is a limitation of data, we instead construct an artificial dataset using a traditional numerical model. The dataset was produced using a combined empirical-analytical-numerical model. Investigations into dataset preparation, feature significance, correlation, and hyperparameter adjustment are conducted. Artificial neural networks, random forests, gradient boosting regression, and decision trees are the machine-learning approaches that have been used to forecast the distribution of hydrogen leaks in the atmosphere. Different error metrics and R2 correlation have been used to assess the prediction accuracy. The RF method was found to be the most effective approach for forecasting the dispersion of hydrogen leaking into the air.
{"title":"Detection of Hydrogen Leakage Using Different Machine Learning Techniques","authors":"M. El-Amin","doi":"10.1109/LT58159.2023.10092303","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092303","url":null,"abstract":"When employing pure hydrogen, its leakage poses a serious safety risk since it can cause fire or explode if it comes into contact with the air. In this study, hydrogen leakage in a form of a buoyant jet is investigated using machine learning approaches. As the experiments used to explore hydrogen leaks are extremely dangerous, and there is a limitation of data, we instead construct an artificial dataset using a traditional numerical model. The dataset was produced using a combined empirical-analytical-numerical model. Investigations into dataset preparation, feature significance, correlation, and hyperparameter adjustment are conducted. Artificial neural networks, random forests, gradient boosting regression, and decision trees are the machine-learning approaches that have been used to forecast the distribution of hydrogen leaks in the atmosphere. Different error metrics and R2 correlation have been used to assess the prediction accuracy. The RF method was found to be the most effective approach for forecasting the dispersion of hydrogen leaking into the air.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114836068","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 : 2023-01-26DOI: 10.1109/LT58159.2023.10092356
Passent El-Kafrawy, Hajer Abbas, Joud AlFarra, Leena Alam, Mehreen Junaid
The term "mobile health" (sometimes spelled "mHealth" or "m-Health") refers to the delivery of medical services via smartphones, tablets, PDAs, and PCs. The metaverse combines the real world and the virtual world, allowing people to interact with their avatars in a setting supported by cutting-edge technologies like high-speed internet, virtual reality, augmented reality, mixed reality, extended reality, blockchain, digital twins, artificial intelligence (AI), all of which are enhanced by practically limitless data. This paper discusses how these technologies might be used in digital medicine in the future, as well as the potential of the medical metaverse. This qualitative study examines and evaluates past articles and websites. The healthcare business depends heavily on physical connection, eye contact, facial expressions, and gestures, which the metaverse can simulate virtually. However, the metaverse may be viewed as a tool to improve the effectiveness of the healthcare system in terms of intervention and treatment, worldwide education, assuring consistent training, and assisting in the development of global databases for research. Finally, the metaverse may be a location where young people can start practicing and acquiring new skills considering how much time they spend in front of screens.
{"title":"The Redefinition of mHealth Applications in the Metaverse","authors":"Passent El-Kafrawy, Hajer Abbas, Joud AlFarra, Leena Alam, Mehreen Junaid","doi":"10.1109/LT58159.2023.10092356","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092356","url":null,"abstract":"The term \"mobile health\" (sometimes spelled \"mHealth\" or \"m-Health\") refers to the delivery of medical services via smartphones, tablets, PDAs, and PCs. The metaverse combines the real world and the virtual world, allowing people to interact with their avatars in a setting supported by cutting-edge technologies like high-speed internet, virtual reality, augmented reality, mixed reality, extended reality, blockchain, digital twins, artificial intelligence (AI), all of which are enhanced by practically limitless data. This paper discusses how these technologies might be used in digital medicine in the future, as well as the potential of the medical metaverse. This qualitative study examines and evaluates past articles and websites. The healthcare business depends heavily on physical connection, eye contact, facial expressions, and gestures, which the metaverse can simulate virtually. However, the metaverse may be viewed as a tool to improve the effectiveness of the healthcare system in terms of intervention and treatment, worldwide education, assuring consistent training, and assisting in the development of global databases for research. Finally, the metaverse may be a location where young people can start practicing and acquiring new skills considering how much time they spend in front of screens.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115913972","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 : 2023-01-26DOI: 10.1109/lt58159.2023.10092367
Biliana Popova
The paper discusses the possibilities for developing open access Islamic education Metaverse platforms. Teaching Islam through multi-participant immersive platforms would enhance pedagogical practices and allow for globalized and de-centralized interactions within and among different Muslim communities around the world. The study contemplates the practical, educational, and theological aspects of using Lifelogging, Immersive Virtual Reality and Mirror Worlds in order to create Metaverse spaces that would enable students of Islam, as well as devotees in general, to learn and practice the main teachings of Islam. The paper also discusses questions regarding the possibility of accurately translating Islamic orthopraxies from a physical to a virtual reality and some of the key challenges that such a translation/transition may pose.
{"title":"Embracing the Metaverse: The Future of Islamic Teaching and Learning","authors":"Biliana Popova","doi":"10.1109/lt58159.2023.10092367","DOIUrl":"https://doi.org/10.1109/lt58159.2023.10092367","url":null,"abstract":"The paper discusses the possibilities for developing open access Islamic education Metaverse platforms. Teaching Islam through multi-participant immersive platforms would enhance pedagogical practices and allow for globalized and de-centralized interactions within and among different Muslim communities around the world. The study contemplates the practical, educational, and theological aspects of using Lifelogging, Immersive Virtual Reality and Mirror Worlds in order to create Metaverse spaces that would enable students of Islam, as well as devotees in general, to learn and practice the main teachings of Islam. The paper also discusses questions regarding the possibility of accurately translating Islamic orthopraxies from a physical to a virtual reality and some of the key challenges that such a translation/transition may pose.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124450646","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 : 2023-01-26DOI: 10.1109/LT58159.2023.10092287
S. Kunhibava, Aishath Muneeza, Zakariya Mustapha, M. Khalid
The use of blockchain technology in financing has been based on its high benefits of efficiency and transparency. However, not many in-depth discussions have been done on such blockchain utilization for Islamic social finance or the metaverse. This paper further explores these three areas of blockchain, the metaverse and Islamic social finance by proposing the use of blockchain and the metaverse for social projects of providing education to the underprivileged with the use of Islamic social finance instruments. In fulfilling this objective this research will explain how blockchain technology was applied in Islamic social finance through the case study of Blossom Smart sukuk, thereafter the opportunities for education under Islamic financial principles through blockchain in metaverse will be explored. The authors believe that further innovation in this area would see renaissance in Islamic social finance both in the real world and in metaverse.
{"title":"Understanding Blockchain technology in Islamic social finance and its opportunities in metaverse","authors":"S. Kunhibava, Aishath Muneeza, Zakariya Mustapha, M. Khalid","doi":"10.1109/LT58159.2023.10092287","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092287","url":null,"abstract":"The use of blockchain technology in financing has been based on its high benefits of efficiency and transparency. However, not many in-depth discussions have been done on such blockchain utilization for Islamic social finance or the metaverse. This paper further explores these three areas of blockchain, the metaverse and Islamic social finance by proposing the use of blockchain and the metaverse for social projects of providing education to the underprivileged with the use of Islamic social finance instruments. In fulfilling this objective this research will explain how blockchain technology was applied in Islamic social finance through the case study of Blossom Smart sukuk, thereafter the opportunities for education under Islamic financial principles through blockchain in metaverse will be explored. The authors believe that further innovation in this area would see renaissance in Islamic social finance both in the real world and in metaverse.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121716462","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 : 2023-01-26DOI: 10.1109/LT58159.2023.10092328
Ammar Alzaydi
This study presents a novel approach to achieve self-balance by employing a proportional integral derivative (PID) control system. Simply put, the innovative design consists of two electric ducted fans (EDF) that propel air against the direction of fall and thereby maintain balance. If these motors are allowed to move in two degrees of freedom, the EDF motors will propel and reduce weight of the two-wheeler while maintaining stability. To the best of our knowledge, no study has proposed a system that simultaneously provides propulsion and weight reduction along with achieving self-balance. The working mechanism of the utilized PID arducopter controller is elucidated which utilizes an IMU sensor and employs a nonlinear complementary filter on the special orthogonal group to determine the lean angles at any instant of time and a feedback loop to maintain the system’s position at the desired upright zero degrees lean angle. Next, the proposed PID controller is first tested on a small-scale model to validate the developed concept to achieve self-balance by employing EDF motors. After achieving successful results on the small-scale model and thereby attaining the step of validation, the proposed concept is tested against a full-scale model (motorbike) by designing the mechanical and electrical parts. The methodology is divided into three major steps – mechanical parts design and manufacture, electrical components design and control system design. Furthermore, three mechanisms are designed to control steering, braking and throttle via a remote transmitter receiver control and autonomous control.
{"title":"Self-Balancing System and Control Design for Two-Wheeled Single-Track Vehicles","authors":"Ammar Alzaydi","doi":"10.1109/LT58159.2023.10092328","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092328","url":null,"abstract":"This study presents a novel approach to achieve self-balance by employing a proportional integral derivative (PID) control system. Simply put, the innovative design consists of two electric ducted fans (EDF) that propel air against the direction of fall and thereby maintain balance. If these motors are allowed to move in two degrees of freedom, the EDF motors will propel and reduce weight of the two-wheeler while maintaining stability. To the best of our knowledge, no study has proposed a system that simultaneously provides propulsion and weight reduction along with achieving self-balance. The working mechanism of the utilized PID arducopter controller is elucidated which utilizes an IMU sensor and employs a nonlinear complementary filter on the special orthogonal group to determine the lean angles at any instant of time and a feedback loop to maintain the system’s position at the desired upright zero degrees lean angle. Next, the proposed PID controller is first tested on a small-scale model to validate the developed concept to achieve self-balance by employing EDF motors. After achieving successful results on the small-scale model and thereby attaining the step of validation, the proposed concept is tested against a full-scale model (motorbike) by designing the mechanical and electrical parts. The methodology is divided into three major steps – mechanical parts design and manufacture, electrical components design and control system design. Furthermore, three mechanisms are designed to control steering, braking and throttle via a remote transmitter receiver control and autonomous control.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128754077","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 : 2023-01-26DOI: 10.1109/LT58159.2023.10092349
Saja Alsulami, Duha Alghamdi, Shahad BinMahfooz, K. Moria
According to the Ministry of Global Health, social distance is one of the most effective defenses against COVID-19 and helps to prevent its spread. Governments have imposed many safety orders on citizens and facilities to limit social distancing and slow the spread of the virus. As a result, there has been an increase in interest in technologies to research and control the spread of COVID-19 in various settings. This research aims to investigate the results of several machine learning approaches to find cases when the physical distance between people has been violated. The method first identifies the instance of the human in the video frame, tracks the movements, computes the distance with other humans on the same frame and thus estimates the number of people who violate the social distance. Compares the approach to performing the performance using Yolo, SSD and Faster R- CNN. Videos that are used in this approach are collected from the wild, considering different camera settings, indoor and outdoor scenes, and recorded from various angles. Comparing the three methods Yolo, SSD and Faster RNN, the results show Yolo has a better performance in detecting humans from the current videos and thus in determining the violation of the distance between humans.
{"title":"Covid -19 Social Distance Analysis Using Machine Learning","authors":"Saja Alsulami, Duha Alghamdi, Shahad BinMahfooz, K. Moria","doi":"10.1109/LT58159.2023.10092349","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092349","url":null,"abstract":"According to the Ministry of Global Health, social distance is one of the most effective defenses against COVID-19 and helps to prevent its spread. Governments have imposed many safety orders on citizens and facilities to limit social distancing and slow the spread of the virus. As a result, there has been an increase in interest in technologies to research and control the spread of COVID-19 in various settings. This research aims to investigate the results of several machine learning approaches to find cases when the physical distance between people has been violated. The method first identifies the instance of the human in the video frame, tracks the movements, computes the distance with other humans on the same frame and thus estimates the number of people who violate the social distance. Compares the approach to performing the performance using Yolo, SSD and Faster R- CNN. Videos that are used in this approach are collected from the wild, considering different camera settings, indoor and outdoor scenes, and recorded from various angles. Comparing the three methods Yolo, SSD and Faster RNN, the results show Yolo has a better performance in detecting humans from the current videos and thus in determining the violation of the distance between humans.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115547383","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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