Pub Date : 2023-06-08DOI: 10.1109/HORA58378.2023.10156659
Iftikhar Ahmad, Ali Ghafail, A. Abdelrhman, S. Chithambaram, S. A. Imam, Mahmood Hammad
Underwater robots are increasingly used for military, commercial, and other applications. They play a crucial role in exploring the oceans and performing tasks that are too dangerous or difficult for humans to do, such as scouting and detecting failures in marine structures/pipelines. However, due to contact-based vector thrust transmission, these robots face the problem of water leakage into their internal circuitry. Therefore, it is necessary to design and develop a fully contactless vector thrust transmission-based autonomous underwater robot with less weight in order to consume less power. In this research, a 3D printed based magnetic coupling system has been designed and developed for autonomous underwater vehicles (AUV). Various components of the magnetic coupling system were 3D printed using Polylactic Acid (PLA) material. Neodymium (NdFeB) magnets were used to develop the magnetic coupling for contactless vector thrust transmission. The magnetic coupling system was successfully tested both in-lab and in a real-time environment without any mishap. It was observed that the 3D printing of the different components reduces the weight of the AUV which helps in the contactless vector thrust transmission.
{"title":"Design and Development of 3D Printed based Magnetic Coupling System for Autonomous Underwater Vehicle","authors":"Iftikhar Ahmad, Ali Ghafail, A. Abdelrhman, S. Chithambaram, S. A. Imam, Mahmood Hammad","doi":"10.1109/HORA58378.2023.10156659","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156659","url":null,"abstract":"Underwater robots are increasingly used for military, commercial, and other applications. They play a crucial role in exploring the oceans and performing tasks that are too dangerous or difficult for humans to do, such as scouting and detecting failures in marine structures/pipelines. However, due to contact-based vector thrust transmission, these robots face the problem of water leakage into their internal circuitry. Therefore, it is necessary to design and develop a fully contactless vector thrust transmission-based autonomous underwater robot with less weight in order to consume less power. In this research, a 3D printed based magnetic coupling system has been designed and developed for autonomous underwater vehicles (AUV). Various components of the magnetic coupling system were 3D printed using Polylactic Acid (PLA) material. Neodymium (NdFeB) magnets were used to develop the magnetic coupling for contactless vector thrust transmission. The magnetic coupling system was successfully tested both in-lab and in a real-time environment without any mishap. It was observed that the 3D printing of the different components reduces the weight of the AUV which helps in the contactless vector thrust transmission.","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":"115131864","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/HORA58378.2023.10156742
N. Nemati, Hadi Farahani, S. R. Kheradpisheh
This paper explores a different method used for market analysis in the Forex stock market. Various econometric models, moving averages, technical indicators, and machine learning techniques have been investigated for predicting stock market trends. This study focuses on designing a new model called the multi-CNN model, which incorporates domain knowledge of Forex. The model is evaluated using EURUSD data from January 2015 to December 2020. The data is preprocessed, normalized, and divided into training, validation, and testing sets. The performance of the proposed model is compared with benchmark models such as Single-LSTM, Single-GRU, and Single-CNN. The results indicate the promising performance of the multi-CNN model in stock market forecasting. The paper provides insights into applying deep learning approaches for predicting stock market trends, highlighting the advantages of combining CNNs and utilizing multiple time frames over simple models such as simple CNN, LSTM, and other recurrent neural network-based models.
{"title":"Stock market prediction by combining CNNs trained on multiple time frames","authors":"N. Nemati, Hadi Farahani, S. R. Kheradpisheh","doi":"10.1109/HORA58378.2023.10156742","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156742","url":null,"abstract":"This paper explores a different method used for market analysis in the Forex stock market. Various econometric models, moving averages, technical indicators, and machine learning techniques have been investigated for predicting stock market trends. This study focuses on designing a new model called the multi-CNN model, which incorporates domain knowledge of Forex. The model is evaluated using EURUSD data from January 2015 to December 2020. The data is preprocessed, normalized, and divided into training, validation, and testing sets. The performance of the proposed model is compared with benchmark models such as Single-LSTM, Single-GRU, and Single-CNN. The results indicate the promising performance of the multi-CNN model in stock market forecasting. The paper provides insights into applying deep learning approaches for predicting stock market trends, highlighting the advantages of combining CNNs and utilizing multiple time frames over simple models such as simple CNN, LSTM, and other recurrent neural network-based models.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"76 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":"125958773","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/HORA58378.2023.10156727
Ozan Yazar, S. Coskun, Lin Li, Feng Zhang, Cong Huang
In the last decade, deep reinforcement learning (DRL) algorithms have been employed in the design of energy management strategy (EMS) for hybrid electric vehicles (HEVs). Investigation of the real-time applicability of DRL algorithms as an EMS is critical in terms of training time, fuel savings, and state-of-charge (SOC) sustainability. To this end, we propose a twin delayed deep deterministic policy gradient (TD3) algorithm that is an improved version of the deep deterministic policy gradient (DDPG) algorithm for HEV fuel savings. Compared to the existing Q-learning-based reinforcement learning and the deep Q-network-based and DDPG-based deep reinforcement algorithms, the proposed TD3 provides stable training efficiency, promising fuel economy, and a lower variation range of SOC charge sustainability under various drive cycles.
{"title":"Actor-Critic TD3-based Deep Reinforcement Learning for Energy Management Strategy of HEV","authors":"Ozan Yazar, S. Coskun, Lin Li, Feng Zhang, Cong Huang","doi":"10.1109/HORA58378.2023.10156727","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156727","url":null,"abstract":"In the last decade, deep reinforcement learning (DRL) algorithms have been employed in the design of energy management strategy (EMS) for hybrid electric vehicles (HEVs). Investigation of the real-time applicability of DRL algorithms as an EMS is critical in terms of training time, fuel savings, and state-of-charge (SOC) sustainability. To this end, we propose a twin delayed deep deterministic policy gradient (TD3) algorithm that is an improved version of the deep deterministic policy gradient (DDPG) algorithm for HEV fuel savings. Compared to the existing Q-learning-based reinforcement learning and the deep Q-network-based and DDPG-based deep reinforcement algorithms, the proposed TD3 provides stable training efficiency, promising fuel economy, and a lower variation range of SOC charge sustainability under various drive cycles.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"62 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":"129546698","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/HORA58378.2023.10156732
Asraa Ahmed Hasan Al_Mashhadani, Timur İnan, A. S. Ahmed
Because of a phenomenon known as the “curs e of dimensionality,” standard machine learning algorithms have difficulty dealing with high-dimensional data. There are more possible examples in the data space as the number of dimensions increases; however, as the number of dimensions increases, the amount of data that can be accessed decreases. There are a greater number of potential instances in the data space when there are more dimensions. The amount of data required by machine learning algorithms to address problems with such a high dimension increases exponentially with the number of problem-related characteristics. In this paper, we examine the suggested algorithms' methods for selecting features and their relationship to the data representation.
{"title":"Data Mining Management System Optimization using Swarm Intelligence","authors":"Asraa Ahmed Hasan Al_Mashhadani, Timur İnan, A. S. Ahmed","doi":"10.1109/HORA58378.2023.10156732","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156732","url":null,"abstract":"Because of a phenomenon known as the “curs e of dimensionality,” standard machine learning algorithms have difficulty dealing with high-dimensional data. There are more possible examples in the data space as the number of dimensions increases; however, as the number of dimensions increases, the amount of data that can be accessed decreases. There are a greater number of potential instances in the data space when there are more dimensions. The amount of data required by machine learning algorithms to address problems with such a high dimension increases exponentially with the number of problem-related characteristics. In this paper, we examine the suggested algorithms' methods for selecting features and their relationship to the data representation.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"19 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":"128560914","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/HORA58378.2023.10156722
Sanaullah Soomro, W. Pora
Ensuring precise power load forecasting is highly important in planning the secure, steady, and cost-effective functioning of the power system. Grid planning and decision-making can be based on accurate long- and short-term power load forecasting. Recently, machine learning techniques have gained wide-spread adoption for both long- and short-term power load forecasting. Specifically, the Long Short-Term Memory (LSTM) is customized for time series data analysis. This research proposes an LSTM model for forecasting the power load of a single house containing electrical appliances over the next 20 days. We conducted a comparative analysis of the impact of dropout layers in load forecasting applications using the LSTM model. The proposed model comprises dropout rates of 0.2, 0.3, 0.4, 0.5, and 0.6, respectively. Their impact on load forecasting is examined. The experimental results demonstrate slight variations in predictions when altering dropout layers. The results show that the effect of dropout layers on the forecast varies the accuracy by only approximately 1%. However, the models with significant dropout rates are more general than those with lower or higher rates. So the model with a dropout rate of 0.4 is suggested.
{"title":"Effect of Drop-out Layers Inside an Long Short-Term Memory for Household Load Forecast Application","authors":"Sanaullah Soomro, W. Pora","doi":"10.1109/HORA58378.2023.10156722","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156722","url":null,"abstract":"Ensuring precise power load forecasting is highly important in planning the secure, steady, and cost-effective functioning of the power system. Grid planning and decision-making can be based on accurate long- and short-term power load forecasting. Recently, machine learning techniques have gained wide-spread adoption for both long- and short-term power load forecasting. Specifically, the Long Short-Term Memory (LSTM) is customized for time series data analysis. This research proposes an LSTM model for forecasting the power load of a single house containing electrical appliances over the next 20 days. We conducted a comparative analysis of the impact of dropout layers in load forecasting applications using the LSTM model. The proposed model comprises dropout rates of 0.2, 0.3, 0.4, 0.5, and 0.6, respectively. Their impact on load forecasting is examined. The experimental results demonstrate slight variations in predictions when altering dropout layers. The results show that the effect of dropout layers on the forecast varies the accuracy by only approximately 1%. However, the models with significant dropout rates are more general than those with lower or higher rates. So the model with a dropout rate of 0.4 is suggested.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"17 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":"123855223","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/HORA58378.2023.10156708
Baraah Alsangari, Göksel Bi̇rci̇k
In order to improve software dependability, Software Fault Prediction (SFP) has become an important research topic in the area of software engineering. To improve program dependability, program defect predictions are being utilized to aid developers in anticipating prospective issues and optimizing testing resources. As a result of this method, the amount of software defects may be forecast, and software testing resources are directed toward the software modules that have the greatest issues, enabling the defects to be fixed as soon as possible. As a result, this paper handles the issue related for SFP based on using a dataset known as JM1 provided by NASA, with 21 features. In this study, several Machine Learning (ML) techniques will be studied, which include Logistic Regression (LR), Random Forest (RF), Naive Bias (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) with three distance metric, Decision Tree (DT). Three cases of normalization will be involved with investigation which are the without sampling, Random over Sample and the SMOTE. Performance evaluation will be based on various parameters such as the ACC, Recall, Precision, and F1-Score. Results obtained indicate that RF achieve the higher ACC with values of 0.81%, 0.92%, and 0.88% respectively. The comprehensive findings of this study may be utilized as a baseline for subsequent studies, allowing any claim of improved prediction using any new approach, model, or framework to be compared and confirmed. In future, the variation of feature number will be involved with performance evaluation in handling SFP.
{"title":"Performance Evaluation of various ML techniques for Software Fault Prediction using NASA dataset","authors":"Baraah Alsangari, Göksel Bi̇rci̇k","doi":"10.1109/HORA58378.2023.10156708","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156708","url":null,"abstract":"In order to improve software dependability, Software Fault Prediction (SFP) has become an important research topic in the area of software engineering. To improve program dependability, program defect predictions are being utilized to aid developers in anticipating prospective issues and optimizing testing resources. As a result of this method, the amount of software defects may be forecast, and software testing resources are directed toward the software modules that have the greatest issues, enabling the defects to be fixed as soon as possible. As a result, this paper handles the issue related for SFP based on using a dataset known as JM1 provided by NASA, with 21 features. In this study, several Machine Learning (ML) techniques will be studied, which include Logistic Regression (LR), Random Forest (RF), Naive Bias (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) with three distance metric, Decision Tree (DT). Three cases of normalization will be involved with investigation which are the without sampling, Random over Sample and the SMOTE. Performance evaluation will be based on various parameters such as the ACC, Recall, Precision, and F1-Score. Results obtained indicate that RF achieve the higher ACC with values of 0.81%, 0.92%, and 0.88% respectively. The comprehensive findings of this study may be utilized as a baseline for subsequent studies, allowing any claim of improved prediction using any new approach, model, or framework to be compared and confirmed. In future, the variation of feature number will be involved with performance evaluation in handling SFP.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"291 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":"123462828","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/HORA58378.2023.10156739
Tsvetelina Mladenova, Vladimir Cankov
The emergence of IoT technology gives much more options for connectivity and intelligence to home appliances. The development of a web-based system for a smart home as a part of an IoT ecosystem gives the ability to control and view different items such as lightning, household appliances, computers, etc. via the Internet, regardless of the user's location. The basic functions that a smart home system should have are security, comfort and convenience, health care, energy consumption and efficiency, and indoor and outdoor care. The smart home is based on the usage of smart appliances and devices and an IoT infrastructure with sensors and controllers, all paired with an application that should have the following functions: alert, monitoring, control and management, and intelligence. This paper proposes a conceptual model for the design and development of an experimental system with four-tier architecture. The first tier consists of the hardware components (sensors, controllers, switches). The second tier is business logic - a software program on a microcomputer or microcontroller, used for communication with the hardware components, as well as to send the data to a server. The third tier is responsible for data management - usually, this is a server or a cloud solution that stores the data in a database. The fourth tier is also software-related and is responsible for the user-interactive part of the whole smart-home system. The graphical interface, with which the user interacts is on this tier, as well as the main business logic of the system, the decision-making rules, and the connection with third-party apps and APIs.
{"title":"Smart Home Based on IoT - Architecture and Practices","authors":"Tsvetelina Mladenova, Vladimir Cankov","doi":"10.1109/HORA58378.2023.10156739","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156739","url":null,"abstract":"The emergence of IoT technology gives much more options for connectivity and intelligence to home appliances. The development of a web-based system for a smart home as a part of an IoT ecosystem gives the ability to control and view different items such as lightning, household appliances, computers, etc. via the Internet, regardless of the user's location. The basic functions that a smart home system should have are security, comfort and convenience, health care, energy consumption and efficiency, and indoor and outdoor care. The smart home is based on the usage of smart appliances and devices and an IoT infrastructure with sensors and controllers, all paired with an application that should have the following functions: alert, monitoring, control and management, and intelligence. This paper proposes a conceptual model for the design and development of an experimental system with four-tier architecture. The first tier consists of the hardware components (sensors, controllers, switches). The second tier is business logic - a software program on a microcomputer or microcontroller, used for communication with the hardware components, as well as to send the data to a server. The third tier is responsible for data management - usually, this is a server or a cloud solution that stores the data in a database. The fourth tier is also software-related and is responsible for the user-interactive part of the whole smart-home system. The graphical interface, with which the user interacts is on this tier, as well as the main business logic of the system, the decision-making rules, and the connection with third-party apps and APIs.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"30 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":"123488070","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/HORA58378.2023.10156690
Israa Salman Atiyah, G. Cansever, A. S. Ahmed
Machine learning is a branch of artificial intelligence based on the idea that systems can learn to identify patterns and make decisions with a minimum of human intervention. In this Paper, demonstration learning will be used, using neural networks in a prototype of a drone built to perform trajectories in controlled environments. To accelerate the training convergence process, a new training data selection approach has been introduced, which picks data from the experience pool based on priority instead of randomness. An autonomous maneuver strategy for dual-UAV olive formation air warfare is provided, which makes use of UAV capabilities such as obstacle avoidance, formation, and confrontation to maximize the effectiveness of the attack.
{"title":"LAN Based GIS Optimization for Coverage in Wireless Networks","authors":"Israa Salman Atiyah, G. Cansever, A. S. Ahmed","doi":"10.1109/HORA58378.2023.10156690","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156690","url":null,"abstract":"Machine learning is a branch of artificial intelligence based on the idea that systems can learn to identify patterns and make decisions with a minimum of human intervention. In this Paper, demonstration learning will be used, using neural networks in a prototype of a drone built to perform trajectories in controlled environments. To accelerate the training convergence process, a new training data selection approach has been introduced, which picks data from the experience pool based on priority instead of randomness. An autonomous maneuver strategy for dual-UAV olive formation air warfare is provided, which makes use of UAV capabilities such as obstacle avoidance, formation, and confrontation to maximize the effectiveness of the attack.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"176 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":"121393757","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/HORA58378.2023.10156755
Omar Alharasees, M. S. Abdalla, Utku Kale
Artificial intelligence (AI) integration in Unmanned Aerial Vehicle (UAV) operations has significantly advanced the field through increased autonomy. However, evaluating the critical aspects of these operations remains a challenge. In order to address this, the current study proposes the use of a combination of the “Observe-Orient-Decide-Act (OODA)” loop and the “Analytic Hierarchy Process (AHP)” method for evaluating AI-UAV systems. The integration of the OODA loop into AHP aims to assess and weigh the critical components of AI-UAV operations, including (i) perception, (ii) decision-making, and (iii) adaptation. The research compares the results of the AHP evaluation between different groups of UAV operators. The findings of this research identify areas for improvement in AI-UAV systems and guide the development of new technologies. In conclusion, this combined approach offers a comprehensive evaluation method for the current and future state of AI-UAV operations, focusing on operator group comparison.
{"title":"Evaluating AI-UAV Systems: A Combined Approach with Operator Group Comparison","authors":"Omar Alharasees, M. S. Abdalla, Utku Kale","doi":"10.1109/HORA58378.2023.10156755","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156755","url":null,"abstract":"Artificial intelligence (AI) integration in Unmanned Aerial Vehicle (UAV) operations has significantly advanced the field through increased autonomy. However, evaluating the critical aspects of these operations remains a challenge. In order to address this, the current study proposes the use of a combination of the “Observe-Orient-Decide-Act (OODA)” loop and the “Analytic Hierarchy Process (AHP)” method for evaluating AI-UAV systems. The integration of the OODA loop into AHP aims to assess and weigh the critical components of AI-UAV operations, including (i) perception, (ii) decision-making, and (iii) adaptation. The research compares the results of the AHP evaluation between different groups of UAV operators. The findings of this research identify areas for improvement in AI-UAV systems and guide the development of new technologies. In conclusion, this combined approach offers a comprehensive evaluation method for the current and future state of AI-UAV operations, focusing on operator group comparison.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"92 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":"121449765","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/HORA58378.2023.10156749
Antonio de Castro, Carlos Coutinho
As technology advances and more of our lives are digitised, elections are no exception. This paper proposes a survey of the current state of the art blockchain voting protocols by performing a systematic literature review and evaluating 25 selected papers. One of the main objectives of the surveyed proposals was to maintain voter anonymity while ensuring vote verifiability. However, most systems fell short in terms of scalability, as they would not be able to handle nationwide elections. Additionally, some proposals identified security vulnerabilities during security testing. Nevertheless, there were promising developments in terms of performance and security through Layer 2 solutions. These solutions offer more flexibility in system design and enable features that ensure anonymity and scalability.
{"title":"Electronic Voting Through Blockchain: A Survey","authors":"Antonio de Castro, Carlos Coutinho","doi":"10.1109/HORA58378.2023.10156749","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156749","url":null,"abstract":"As technology advances and more of our lives are digitised, elections are no exception. This paper proposes a survey of the current state of the art blockchain voting protocols by performing a systematic literature review and evaluating 25 selected papers. One of the main objectives of the surveyed proposals was to maintain voter anonymity while ensuring vote verifiability. However, most systems fell short in terms of scalability, as they would not be able to handle nationwide elections. Additionally, some proposals identified security vulnerabilities during security testing. Nevertheless, there were promising developments in terms of performance and security through Layer 2 solutions. These solutions offer more flexibility in system design and enable features that ensure anonymity and scalability.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"4 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":"128012769","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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