Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10066991
Moo Geon Kim, Seong An Kang, M. Ryu
This study attempted an ANOVA analysis to see the status of various omni-channel usage by customer type using actual customer data of domestic provincial banks. And, through regression analysis, the factors influencing each channel were investigated. Therefore, the purpose of study provides implications for rethinking banks' branch closure strategies through this analysis. As a result of the analysis, it was found that the older the customer, the higher the customer class, the more they use face-to-face(branch )channels.
{"title":"Rethinking bank branch closure strategies through omni-channel usage data analysis","authors":"Moo Geon Kim, Seong An Kang, M. Ryu","doi":"10.1109/ICAIIC57133.2023.10066991","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066991","url":null,"abstract":"This study attempted an ANOVA analysis to see the status of various omni-channel usage by customer type using actual customer data of domestic provincial banks. And, through regression analysis, the factors influencing each channel were investigated. Therefore, the purpose of study provides implications for rethinking banks' branch closure strategies through this analysis. As a result of the analysis, it was found that the older the customer, the higher the customer class, the more they use face-to-face(branch )channels.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115214586","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 : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067070
Anh-Tien Tran, D. Lakew, D. Hua, Quang Tuan Do, Nhu-Ngoc Dao, Sungrae Cho
Rate-splitting multiple access (RSMA) is acknowledged as a promising solution for improving the capacity of dense downlink networks designed to meet the severe criteria of networks beyond 5G, in which a large number of users may be simultaneously supplied by a single bandwidth spectrum. Numerous studies have focused on establishing acceptable RSMA solutions for energy optimization measures like energy efficiency (EE) and weighted power consumption of all users under the acceptance of perfect or imperfect channel state information at transmitter (CSIT). This evaluation focuses on the technical features of newly published papers and their relevance in a variety of situations. We also cover the difficulties and unresolved concerns of RSMA applications in future heterogeneous downlink networks.
{"title":"A Review on Rate-Splitting Multiple Access-Assisted Downlink Networks: Energy Optimizations","authors":"Anh-Tien Tran, D. Lakew, D. Hua, Quang Tuan Do, Nhu-Ngoc Dao, Sungrae Cho","doi":"10.1109/ICAIIC57133.2023.10067070","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067070","url":null,"abstract":"Rate-splitting multiple access (RSMA) is acknowledged as a promising solution for improving the capacity of dense downlink networks designed to meet the severe criteria of networks beyond 5G, in which a large number of users may be simultaneously supplied by a single bandwidth spectrum. Numerous studies have focused on establishing acceptable RSMA solutions for energy optimization measures like energy efficiency (EE) and weighted power consumption of all users under the acceptance of perfect or imperfect channel state information at transmitter (CSIT). This evaluation focuses on the technical features of newly published papers and their relevance in a variety of situations. We also cover the difficulties and unresolved concerns of RSMA applications in future heterogeneous downlink networks.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116686740","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 : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067044
Van Linh Nguyen, Duc Hoang Tran, Huy Nguyen, ByungDeok Chung, Y. Jang
The Internet of Things (IoT), satellite communication, and other communication systems that utilize radio frequency waveforms frequently use modern wireless communication technologies. Wireless communication provides benefits over conventional communication due to its simple installation. One of the most well-known of them is Optical Camera Communication (OCC) technology, which has several advantages, including no negative effects on human health, good security, and inexpensive operating expenses. In this paper, we offer a multiple-input multiple-output modulation method that uses deep learning (DL) to recognize light-emitting diodes and predict thresholds while considering mobility assistance and long-distance communication into considerations. Our suggested strategy employs DL algorithms to enhance the performance of the traditional camera on-off keying (C-OOK) scheme by reducing bit error rate, communication distance., and data rate.
{"title":"Development of MIMO Scheme-based Optical Camera Communication System using Deep Learning method","authors":"Van Linh Nguyen, Duc Hoang Tran, Huy Nguyen, ByungDeok Chung, Y. Jang","doi":"10.1109/ICAIIC57133.2023.10067044","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067044","url":null,"abstract":"The Internet of Things (IoT), satellite communication, and other communication systems that utilize radio frequency waveforms frequently use modern wireless communication technologies. Wireless communication provides benefits over conventional communication due to its simple installation. One of the most well-known of them is Optical Camera Communication (OCC) technology, which has several advantages, including no negative effects on human health, good security, and inexpensive operating expenses. In this paper, we offer a multiple-input multiple-output modulation method that uses deep learning (DL) to recognize light-emitting diodes and predict thresholds while considering mobility assistance and long-distance communication into considerations. Our suggested strategy employs DL algorithms to enhance the performance of the traditional camera on-off keying (C-OOK) scheme by reducing bit error rate, communication distance., and data rate.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116898890","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 : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067003
Donghyun Lee, Yong-Kyu Jeon, Junsuk Oh, Chunghyun Lee, Taeyun Ha, Sungrae Cho
In this paper, various remaining useful life (RUL) studies were investigated to implement a prognostics and health management (PHM) system that monitors and predicts failures of machines using vast industrial data obtained through the development of IoT. We introduce prediction techniques and analyze cases applied to the aviation and shipping industries to investigate RUL technology that can be applied to various industries in the future.
{"title":"Preventive Maintenance Techniques through Learning-based Remaining Useful Lifetime Prediction in IoT Sensor Networks: The Survey","authors":"Donghyun Lee, Yong-Kyu Jeon, Junsuk Oh, Chunghyun Lee, Taeyun Ha, Sungrae Cho","doi":"10.1109/ICAIIC57133.2023.10067003","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067003","url":null,"abstract":"In this paper, various remaining useful life (RUL) studies were investigated to implement a prognostics and health management (PHM) system that monitors and predicts failures of machines using vast industrial data obtained through the development of IoT. We introduce prediction techniques and analyze cases applied to the aviation and shipping industries to investigate RUL technology that can be applied to various industries in the future.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116996388","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 : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067049
Venkata Vivek Gowripeddi, G. Sasirekha, Jyotsna L. Bapat, D. Das
Industry 4.0 brings about automation of smart factories, where the factory operations can be monitored and controlled remotely. This automation enhances the work flow efficiency. However, the Industry 4.0 associated digitization and networking in the smart factories makes them vulnerable to cyberattacks, because of the usage of weak passwords, open-source software, and communication protocols used in building them. These vulnerabilities make Distributed Denial of Service (DDoS) attacks plausible. DDoS attacks can not only disrupt the normal operations, but also cost in terms of the brand-name, trust, and reputation loss. The solution is to quickly detect and mitigate these attacks. This paper describes a Digital Twin (DT) based approach for detection of DDoS cyber-attacks in smart factories. An ontology-based intrusion detection system is proposed, in which the DT that replicates the physical system, learns the normal operation of the physical network, and remembers it. Whenever the physical system's Quality of Service (QoS) metrics deviate from normality, an automated query to the knowledge base generates an alert. This paper presents the architecture and the functional test results of the prototype developed. This prototype has the advantages of context awareness, re-usability of model in complex contexts, and support for Relational Database (RD).
{"title":"Digital Twin and Ontology based DDoS Attack Detection in a Smart-Factory 4.0","authors":"Venkata Vivek Gowripeddi, G. Sasirekha, Jyotsna L. Bapat, D. Das","doi":"10.1109/ICAIIC57133.2023.10067049","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067049","url":null,"abstract":"Industry 4.0 brings about automation of smart factories, where the factory operations can be monitored and controlled remotely. This automation enhances the work flow efficiency. However, the Industry 4.0 associated digitization and networking in the smart factories makes them vulnerable to cyberattacks, because of the usage of weak passwords, open-source software, and communication protocols used in building them. These vulnerabilities make Distributed Denial of Service (DDoS) attacks plausible. DDoS attacks can not only disrupt the normal operations, but also cost in terms of the brand-name, trust, and reputation loss. The solution is to quickly detect and mitigate these attacks. This paper describes a Digital Twin (DT) based approach for detection of DDoS cyber-attacks in smart factories. An ontology-based intrusion detection system is proposed, in which the DT that replicates the physical system, learns the normal operation of the physical network, and remembers it. Whenever the physical system's Quality of Service (QoS) metrics deviate from normality, an automated query to the knowledge base generates an alert. This paper presents the architecture and the functional test results of the prototype developed. This prototype has the advantages of context awareness, re-usability of model in complex contexts, and support for Relational Database (RD).","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127332672","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 : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10066963
Seunghwan Bae, Dongun Han, Seongkeun Park
In this paper, we introduce an object detection model that combines a camera and a LiDAR sensor. In previous object detection studies have mainly focused on using one sensor, and mainly camera and LiDAR sensors were used. Research was mainly conducted in the direction of utilizing a single sensor, and typically cameras and LiDAR sensors were used. However, Camera and Li-DAR sensors have disadvantages such as being vulnerable to environmental changes or having sparse expressive power, so the method to improve them is needed for a stable cognitive system. In this paper, we propose the LiDAR Camera Fusion Network, a sensor fusion object detection model that uses the advantages of each sensor to improve the disadvantages of cameras and Li-DAR sensors. The sensor fusion object detector developed in this study has the feature of estimating the location of an object through LiDAR Clustering. Extraction speed is about 58 times faster than Selective search without prior learning, reducing the number of candidate regions from 2000 to 98, despite reducing the number of candidate regions, compared to existing methods, the ratio of the correct answer candidate areas among the total location candidate regions was 10 times larger. Due to the above characteristics, efficient learning and inference were possible compared to the existing method, and this model finally extracts the probability value of the object, the bounding box correction value, and the distance value from the object. Due to the characteristic of our research, we used KITTI data because LiDAR and image data were needed. As a result, we compare the results with object detection models that are often used in the object detection area.
{"title":"A New Approach to Lidar and Camera Fusion for Autonomous Driving","authors":"Seunghwan Bae, Dongun Han, Seongkeun Park","doi":"10.1109/ICAIIC57133.2023.10066963","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066963","url":null,"abstract":"In this paper, we introduce an object detection model that combines a camera and a LiDAR sensor. In previous object detection studies have mainly focused on using one sensor, and mainly camera and LiDAR sensors were used. Research was mainly conducted in the direction of utilizing a single sensor, and typically cameras and LiDAR sensors were used. However, Camera and Li-DAR sensors have disadvantages such as being vulnerable to environmental changes or having sparse expressive power, so the method to improve them is needed for a stable cognitive system. In this paper, we propose the LiDAR Camera Fusion Network, a sensor fusion object detection model that uses the advantages of each sensor to improve the disadvantages of cameras and Li-DAR sensors. The sensor fusion object detector developed in this study has the feature of estimating the location of an object through LiDAR Clustering. Extraction speed is about 58 times faster than Selective search without prior learning, reducing the number of candidate regions from 2000 to 98, despite reducing the number of candidate regions, compared to existing methods, the ratio of the correct answer candidate areas among the total location candidate regions was 10 times larger. Due to the above characteristics, efficient learning and inference were possible compared to the existing method, and this model finally extracts the probability value of the object, the bounding box correction value, and the distance value from the object. Due to the characteristic of our research, we used KITTI data because LiDAR and image data were needed. As a result, we compare the results with object detection models that are often used in the object detection area.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123279437","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 : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10066987
Hendrik Leo, Khairun Saddami, Roslidar, R. Muharar, K. Munadi, F. Arnia
Obesity is one of the major risk factors for non-communicable diseases. Developing an early obese screening method is crucial to facilitate the early treatment of obese patients. In this study, we proposed a stand-alone mobile application for early diagnosis of obesity based on Convolution Neural Network (CNN) classifier model. The proposed CNN model was developed based on MobileNetV2 by modifying the fully connected layers. We trained the proposed model with the obese thermogram dataset through the transfer learning method and compared the classification performances with pre-trained models. The testing results show that the proposed model achieved an accuracy of 87.50%, a specificity of 100 %, and a sensitivity of 75.00 %. The proposed model demonstrated an optimal fit learning with 2.5 million learning parameters, a computation cost of 0.613 GFLOPs, and a size of 9.8 MB. The proposed model has been deployed and tested into the thermal camera smartphone CAT S62 Pro to do an early diagnosis of obesity.
{"title":"A Mobile Application for Obesity Early Diagnosis Using CNN-based Thermogram Classification","authors":"Hendrik Leo, Khairun Saddami, Roslidar, R. Muharar, K. Munadi, F. Arnia","doi":"10.1109/ICAIIC57133.2023.10066987","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066987","url":null,"abstract":"Obesity is one of the major risk factors for non-communicable diseases. Developing an early obese screening method is crucial to facilitate the early treatment of obese patients. In this study, we proposed a stand-alone mobile application for early diagnosis of obesity based on Convolution Neural Network (CNN) classifier model. The proposed CNN model was developed based on MobileNetV2 by modifying the fully connected layers. We trained the proposed model with the obese thermogram dataset through the transfer learning method and compared the classification performances with pre-trained models. The testing results show that the proposed model achieved an accuracy of 87.50%, a specificity of 100 %, and a sensitivity of 75.00 %. The proposed model demonstrated an optimal fit learning with 2.5 million learning parameters, a computation cost of 0.613 GFLOPs, and a size of 9.8 MB. The proposed model has been deployed and tested into the thermal camera smartphone CAT S62 Pro to do an early diagnosis of obesity.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115036866","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 : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067038
Revin Naufal Alief, Made Adi Paramartha Putra, Augustin Gohil, Jae-Min Lee, Dong‐Seong Kim
The usage of the federated learning (FL) concept in the artificial intelligence (AI) field has increased. The main concept of FL is to tackle the centralized-based approach, which requires the model to update training data to the cloud server by creating a decentralized deep learning (DL) model. However, the current FL model is still not completely decentralized, as each client needs to upload the training data to a centralized aggregator. Thus, this paper proposed an implementation of the FL scheme by using blockchain to tackle this problem. The proposed system uses the blockchain as the place to exchange training data instead of sending the training data immediately to the aggregator. In addition, this paper also tried to implement the layer 2 blockchain to minimize the time needed to exchange training information between each client and aggregator. The simulation result of this paper shows that we are able to implement the layer 2 blockchain in the FL system successfully. Also, it is shown that by using the layer 2 blockchain, training data exchange time is able to be reduced by around 50% compared to the layer 1 blockchain. In addition, this paper shows that the implementation of the layer 2 blockchain does not affect the performance of the FL model in terms of accuracy.
{"title":"FLB2: Layer 2 Blockchain Implementation Scheme on Federated Learning Technique","authors":"Revin Naufal Alief, Made Adi Paramartha Putra, Augustin Gohil, Jae-Min Lee, Dong‐Seong Kim","doi":"10.1109/ICAIIC57133.2023.10067038","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067038","url":null,"abstract":"The usage of the federated learning (FL) concept in the artificial intelligence (AI) field has increased. The main concept of FL is to tackle the centralized-based approach, which requires the model to update training data to the cloud server by creating a decentralized deep learning (DL) model. However, the current FL model is still not completely decentralized, as each client needs to upload the training data to a centralized aggregator. Thus, this paper proposed an implementation of the FL scheme by using blockchain to tackle this problem. The proposed system uses the blockchain as the place to exchange training data instead of sending the training data immediately to the aggregator. In addition, this paper also tried to implement the layer 2 blockchain to minimize the time needed to exchange training information between each client and aggregator. The simulation result of this paper shows that we are able to implement the layer 2 blockchain in the FL system successfully. Also, it is shown that by using the layer 2 blockchain, training data exchange time is able to be reduced by around 50% compared to the layer 1 blockchain. In addition, this paper shows that the implementation of the layer 2 blockchain does not affect the performance of the FL model in terms of accuracy.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115038046","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 : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067040
Paul Vincent Llanes, Geoffrey A. Solano, Marc Jermaine Pontiveros
Ahstract-SARS-CoV-2 virus has long been evolving posing an increased risk in terms of infectivity and transmissibility which causes greater impact in communities worldwide. With the surge of collected SARS-CoV-2 sequences, studies found out that most of the emerging variants are linked to increased mutations in the spike (S) protein as observed in Alpha, Beta, Gamma, and Delta variants. Multiple approaches on genomic surveillance have been performed to monitor the mutational status and spread of the virus however most are heavily dependent on labels attributed to these sequences. Hence, this study features a system that has the capability to learn the protein language model of SARS-CoV-2 spike proteins, based on a bidirectional long-short term memory (BiLSTM) recurrent neural network, using sequence data alone. Upon obtaining the sequence embedding from the model, observed clusters are generated using the Leiden clustering algorithm and is visualized to monitor similarities between variants in terms of grammatical probability and semantic change. Additionally, the system measures the validity of a user-generated next-generation sequence capturing potential sequence mutations indicative of viral escape, particularly mutations by substitutions. Further studies on methods uncovering semantic rules that govern spike proteins are recommended to learn more about other viral characteristics conclusive of the future of the COVID-19 pandemic.
{"title":"Learning the Protein Language Model of SARS-CoV-2 Spike Proteins","authors":"Paul Vincent Llanes, Geoffrey A. Solano, Marc Jermaine Pontiveros","doi":"10.1109/ICAIIC57133.2023.10067040","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067040","url":null,"abstract":"Ahstract-SARS-CoV-2 virus has long been evolving posing an increased risk in terms of infectivity and transmissibility which causes greater impact in communities worldwide. With the surge of collected SARS-CoV-2 sequences, studies found out that most of the emerging variants are linked to increased mutations in the spike (S) protein as observed in Alpha, Beta, Gamma, and Delta variants. Multiple approaches on genomic surveillance have been performed to monitor the mutational status and spread of the virus however most are heavily dependent on labels attributed to these sequences. Hence, this study features a system that has the capability to learn the protein language model of SARS-CoV-2 spike proteins, based on a bidirectional long-short term memory (BiLSTM) recurrent neural network, using sequence data alone. Upon obtaining the sequence embedding from the model, observed clusters are generated using the Leiden clustering algorithm and is visualized to monitor similarities between variants in terms of grammatical probability and semantic change. Additionally, the system measures the validity of a user-generated next-generation sequence capturing potential sequence mutations indicative of viral escape, particularly mutations by substitutions. Further studies on methods uncovering semantic rules that govern spike proteins are recommended to learn more about other viral characteristics conclusive of the future of the COVID-19 pandemic.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116127231","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 : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067055
Takashi Imanaka, M. Ohta, M. Taromaru
In wireless communication systems that perform carrier sense, packet collisions due to simultaneous transmission between transmitting nodes frequently occur because transmission starts the moment the channel becomes idle. In wireless LANs and other systems, backoff algorithms are used to avoid simultaneous transmission, but the commonly used binary backoff results in excessively large waiting times due to random backoff. Therefore, this paper proposes a new channel access control method using reinforcement learning. Simulation evaluation shows the effectiveness of the proposed method by the characteristics of the transmission success rate.
{"title":"Channel Access Control Instead of Random Backoff Algorithm","authors":"Takashi Imanaka, M. Ohta, M. Taromaru","doi":"10.1109/ICAIIC57133.2023.10067055","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067055","url":null,"abstract":"In wireless communication systems that perform carrier sense, packet collisions due to simultaneous transmission between transmitting nodes frequently occur because transmission starts the moment the channel becomes idle. In wireless LANs and other systems, backoff algorithms are used to avoid simultaneous transmission, but the commonly used binary backoff results in excessively large waiting times due to random backoff. Therefore, this paper proposes a new channel access control method using reinforcement learning. Simulation evaluation shows the effectiveness of the proposed method by the characteristics of the transmission success rate.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114276045","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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