Pub Date : 2023-01-26DOI: 10.1109/LT58159.2023.10092345
Sammar Z Allam
This research explores machine learnt use-patter of human activities in outdoor and indoor spaces. It proposes scenarios to be used through Generative Adversarial Network for AI-Based use-pattern generative spaces. The study investigates futuristic AI-based life styles and its impact on the architectural spaces and clusters agglomeration. Clusters algorithmic design includes cellular automata and fractals in regards of stable null space as green nodes. Machine learnt use-pattern from daily activities incorporates a dynamic-responsive pixelation of spaces. The research manifest machine learnt algorithmic design and demonstrates IOT generative responsive spaces. A hybrid space of extended spatial reality that complies as well architectural boundaries has resulted using cellular responsive units/cells to include an extended reality (XR) part that supply the space with extended multiple activities. This hybrid space acts as a virtual and a real space that accommodates various functionalities with flexibilities relying on IOT, AI-based and XR technologies.
{"title":"AI-Based Use-Pattern Generative Hybrid Spaces for Indoor and Outdoor Activities","authors":"Sammar Z Allam","doi":"10.1109/LT58159.2023.10092345","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092345","url":null,"abstract":"This research explores machine learnt use-patter of human activities in outdoor and indoor spaces. It proposes scenarios to be used through Generative Adversarial Network for AI-Based use-pattern generative spaces. The study investigates futuristic AI-based life styles and its impact on the architectural spaces and clusters agglomeration. Clusters algorithmic design includes cellular automata and fractals in regards of stable null space as green nodes. Machine learnt use-pattern from daily activities incorporates a dynamic-responsive pixelation of spaces. The research manifest machine learnt algorithmic design and demonstrates IOT generative responsive spaces. A hybrid space of extended spatial reality that complies as well architectural boundaries has resulted using cellular responsive units/cells to include an extended reality (XR) part that supply the space with extended multiple activities. This hybrid space acts as a virtual and a real space that accommodates various functionalities with flexibilities relying on IOT, AI-based and XR technologies.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115911412","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":null,"pages":null},"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}
Pub Date : 2023-01-26DOI: 10.1109/LT58159.2023.10092357
Mirna Ali, Nouf Alsaedi, S. Qaisar
People with disabilities struggle to perform specific tasks throughout their daily life. However, BCI systems are developed to assist people struggling with motor impairment by transforming their thoughts into action. Non-invasive BCI systems use electroencephalogram (EEG) to record brain activities. In this study, we segment the EEG signals and then break the segment down into a few intrinsic mode functions using oscillation mode decomposition. Then the intrinsic mode functions are mined for feature extraction. The features mined are processed by different machine learning algorithms for categorization. Among the different algorithms, K-NN yielded the best results with an overall average accuracy score of 95.48%. This approach can be used in future to develop the brain driven metaverse interactive solutions.
{"title":"Non-Invasive BCI by using EMD and Machine Learning: A Metaverse Interaction Perspective","authors":"Mirna Ali, Nouf Alsaedi, S. Qaisar","doi":"10.1109/LT58159.2023.10092357","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092357","url":null,"abstract":"People with disabilities struggle to perform specific tasks throughout their daily life. However, BCI systems are developed to assist people struggling with motor impairment by transforming their thoughts into action. Non-invasive BCI systems use electroencephalogram (EEG) to record brain activities. In this study, we segment the EEG signals and then break the segment down into a few intrinsic mode functions using oscillation mode decomposition. Then the intrinsic mode functions are mined for feature extraction. The features mined are processed by different machine learning algorithms for categorization. Among the different algorithms, K-NN yielded the best results with an overall average accuracy score of 95.48%. This approach can be used in future to develop the brain driven metaverse interactive solutions.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115753622","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":null,"pages":null},"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/LT58159.2023.10092295
Mai El Seknedy, S. Fawzi
The Speech Emotion Recognition (SER) system is an approach to identify individuals' emotions. This is important for human-machine interface applications and for the emerging Metaverse. This work presents a bilingual Arabic-English speech emotion recognition system based on EYASE and RAVDESS datasets. A novel feature set was composed by using spectral and prosodic parameters to obtain high performance at a low computational cost. Different classification models were applied. These machine learning classifiers are Random Forest, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, and Ensemble learning. The proposed feature set performance was compared to the "Interspeech 2009" challenge feature set, which is considered a benchmark in the field. Promising results were obtained using the proposed feature sets. SVM resulted in the best emotion recognition rate and execution performance. The best accuracies achieved were 85% on RADVESS, and 64% on EYASE. Ensemble learning detected the valence emotion with 90% on RADVESS, and 87.6% on EYASE.
{"title":"Arabic English Speech Emotion Recognition System","authors":"Mai El Seknedy, S. Fawzi","doi":"10.1109/LT58159.2023.10092295","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092295","url":null,"abstract":"The Speech Emotion Recognition (SER) system is an approach to identify individuals' emotions. This is important for human-machine interface applications and for the emerging Metaverse. This work presents a bilingual Arabic-English speech emotion recognition system based on EYASE and RAVDESS datasets. A novel feature set was composed by using spectral and prosodic parameters to obtain high performance at a low computational cost. Different classification models were applied. These machine learning classifiers are Random Forest, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, and Ensemble learning. The proposed feature set performance was compared to the \"Interspeech 2009\" challenge feature set, which is considered a benchmark in the field. Promising results were obtained using the proposed feature sets. SVM resulted in the best emotion recognition rate and execution performance. The best accuracies achieved were 85% on RADVESS, and 64% on EYASE. Ensemble learning detected the valence emotion with 90% on RADVESS, and 87.6% on EYASE.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134442648","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/lt58159.2023.10092342
Erika Mazurkiewicz, Sahar Al Seesi, Amal Abdel Raouf
The spread of COVID-19 has thrown the world into a panic. We are constantly learning more about the virus every day, from how it spreads to who is more susceptible to becoming infected by different variants. Those with underlying respiratory conditions and other immunocompromised individuals need to be extra cautious regarding the virus. Many researchers have created COVID-19 trackers to detect the spread of COVID-19 around the world and show hot spots where COVID-19 cases are more prevalent. Previous work lacks the consideration of comorbidity as a factor of death rate. This work aims to create an agent-based model to predict comorbidity death rate caused by a health condition in addition to COVID-19. The model is evaluated using the symmetric mean absolute percentage error metric and proved to be very efficient.
{"title":"Predicting COVID-19 Mortalities for Patients with Special Health Conditions Using an Agent-Based Model","authors":"Erika Mazurkiewicz, Sahar Al Seesi, Amal Abdel Raouf","doi":"10.1109/lt58159.2023.10092342","DOIUrl":"https://doi.org/10.1109/lt58159.2023.10092342","url":null,"abstract":"The spread of COVID-19 has thrown the world into a panic. We are constantly learning more about the virus every day, from how it spreads to who is more susceptible to becoming infected by different variants. Those with underlying respiratory conditions and other immunocompromised individuals need to be extra cautious regarding the virus. Many researchers have created COVID-19 trackers to detect the spread of COVID-19 around the world and show hot spots where COVID-19 cases are more prevalent. Previous work lacks the consideration of comorbidity as a factor of death rate. This work aims to create an agent-based model to predict comorbidity death rate caused by a health condition in addition to COVID-19. The model is evaluated using the symmetric mean absolute percentage error metric and proved to be very efficient.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115496238","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/LT58159.2023.10092310
M. El-Amin, Budoor Alwated
The use of machine learning to forecast how nanoparticles would migrate through porous material is covered in this research. We employed the random forest, decision tree, artificial neural network, and gradient boosting regression machine learning techniques. Since there are not many experimental datasets available, it is easier to create artificial datasets using verified numerical simulators. Additionally, covered in the paper are data preprocessing, correlations, the importance of features, and hyperparameter adjustment. Moreover, different error metrics and R2-correlation are used to gauge how well the predictive models perform. Finally, examples of the findings are presented. The decision tree model is determined to have the highest accuracy, the best performance, and the lowest root mean squared error.
{"title":"Machine Learning Prediction for Nanoparticles Behavior in Hydrocarbon Reservoirs","authors":"M. El-Amin, Budoor Alwated","doi":"10.1109/LT58159.2023.10092310","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092310","url":null,"abstract":"The use of machine learning to forecast how nanoparticles would migrate through porous material is covered in this research. We employed the random forest, decision tree, artificial neural network, and gradient boosting regression machine learning techniques. Since there are not many experimental datasets available, it is easier to create artificial datasets using verified numerical simulators. Additionally, covered in the paper are data preprocessing, correlations, the importance of features, and hyperparameter adjustment. Moreover, different error metrics and R2-correlation are used to gauge how well the predictive models perform. Finally, examples of the findings are presented. The decision tree model is determined to have the highest accuracy, the best performance, and the lowest root mean squared error.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127013885","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/LT58159.2023.10092283
A. Hussein, Ibrahim Salah, K. Rahouma, M. Mourad Mabrook
The rapidly increasing demand for mobile communication over satellite platforms and its applications necessitates a significant effort on the part of researchers to fulfill the prospective requirements of wireless network infrastructure. It is predicted that traffic will increase by multiples of hundreds soon. Therefore, the network's capacity has to multiply with high energy efficiency (EE), which can be achieved using massive multiple-input, multiple-output (M-MIMO). An adaptive scheme that maximizes energy efficiency is proposed in this paper at maximum spectral efficiency. Also, an efficient tradeoff between energy efficiency and throughput is mainly proposed. The analytical and simulation results prove that the proposed multi-cell minimum mean square error (M-MMSE) precoding scheme provides the maximum EE and efficient throughput of next-generation networks and satellite communication utilizing M- MIMO. Hence, it gives the optimum and most efficient tradeoff between EE and the throughput of the M-MIMO system.
{"title":"Efficient Tradeoff between Throughput and Energy Efficiency of Massive-MIMO Technique for Satellite Communication applications","authors":"A. Hussein, Ibrahim Salah, K. Rahouma, M. Mourad Mabrook","doi":"10.1109/LT58159.2023.10092283","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092283","url":null,"abstract":"The rapidly increasing demand for mobile communication over satellite platforms and its applications necessitates a significant effort on the part of researchers to fulfill the prospective requirements of wireless network infrastructure. It is predicted that traffic will increase by multiples of hundreds soon. Therefore, the network's capacity has to multiply with high energy efficiency (EE), which can be achieved using massive multiple-input, multiple-output (M-MIMO). An adaptive scheme that maximizes energy efficiency is proposed in this paper at maximum spectral efficiency. Also, an efficient tradeoff between energy efficiency and throughput is mainly proposed. The analytical and simulation results prove that the proposed multi-cell minimum mean square error (M-MMSE) precoding scheme provides the maximum EE and efficient throughput of next-generation networks and satellite communication utilizing M- MIMO. Hence, it gives the optimum and most efficient tradeoff between EE and the throughput of the M-MIMO system.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125922466","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/LT58159.2023.10092319
Dev Mashru, Ganesha Maruthi Mangipudi, H. Swamy, Shivakumar Halangali, Sushma E
Instant messaging applications enable users to communicate with each other in a simple and efficient manner. However, big corporations dominate and control the market today. This gives these corporations authority over their users and their data. Moreover, all popular instant messaging applications today rely on a set of centralised servers, introducing a single point of failure. The authors of this paper propose an instant messaging application that relies on a decentralised architecture, a first of its kind, hosted on the cloud, making the service fault-tolerant. Additionally, all messages being exchanged between users are End-to-End Encrypted, providing the users with privacy. The authors of this paper were able to demonstrate the proposed system working as envisioned.
{"title":"A Decentralised Instant Messaging Application with End-to-End Encryption","authors":"Dev Mashru, Ganesha Maruthi Mangipudi, H. Swamy, Shivakumar Halangali, Sushma E","doi":"10.1109/LT58159.2023.10092319","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092319","url":null,"abstract":"Instant messaging applications enable users to communicate with each other in a simple and efficient manner. However, big corporations dominate and control the market today. This gives these corporations authority over their users and their data. Moreover, all popular instant messaging applications today rely on a set of centralised servers, introducing a single point of failure. The authors of this paper propose an instant messaging application that relies on a decentralised architecture, a first of its kind, hosted on the cloud, making the service fault-tolerant. Additionally, all messages being exchanged between users are End-to-End Encrypted, providing the users with privacy. The authors of this paper were able to demonstrate the proposed system working as envisioned.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126021483","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/LT58159.2023.10092368
Esraa Elgamal, Walaa Medhat, M. A. Elfatah, Nashwa Abdelbaki
In recent years, advances in technology in several industries have resulted in massive data collections on the web. It raises worries about large data security and protection. The advent of Blockchain technology has caused a revolution in the security field for different applications. The distributed ledger is stored on each Blockchain node, which enhances security and data transparency. On the Blockchain network, illegal users are not authorized to undertake any fault transactions. In this article, we will discuss how Blockchain may be employed to secure the big data. We explain the problems that the Blockchain faced with big data and its solutions. We summarize recent works of Blockchain with big data and the present issues and trends. We demonstrate that Blockchain technology is still in its initial phases of validation and there are no large-scale application scenarios available, particularly in the big data sector. Finally, we narrow our study to the Healthcare industry and offer the following research directions for its primary issues.
{"title":"Blockchain Application on Big Data Security","authors":"Esraa Elgamal, Walaa Medhat, M. A. Elfatah, Nashwa Abdelbaki","doi":"10.1109/LT58159.2023.10092368","DOIUrl":"https://doi.org/10.1109/LT58159.2023.10092368","url":null,"abstract":"In recent years, advances in technology in several industries have resulted in massive data collections on the web. It raises worries about large data security and protection. The advent of Blockchain technology has caused a revolution in the security field for different applications. The distributed ledger is stored on each Blockchain node, which enhances security and data transparency. On the Blockchain network, illegal users are not authorized to undertake any fault transactions. In this article, we will discuss how Blockchain may be employed to secure the big data. We explain the problems that the Blockchain faced with big data and its solutions. We summarize recent works of Blockchain with big data and the present issues and trends. We demonstrate that Blockchain technology is still in its initial phases of validation and there are no large-scale application scenarios available, particularly in the big data sector. Finally, we narrow our study to the Healthcare industry and offer the following research directions for its primary issues.","PeriodicalId":142898,"journal":{"name":"2023 20th Learning and Technology Conference (L&T)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128576910","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：