2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献

Rethinking bank branch closure strategies through omni-channel usage data analysis 通过全渠道使用数据分析重新思考银行网点关闭策略

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10066991

Moo Geon Kim, Seong An Kang, M. Ryu

引用次数: 0

A Review on Rate-Splitting Multiple Access-Assisted Downlink Networks: Energy Optimizations 速率分割多接入辅助下行网络的研究进展:能量优化

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067070

Anh-Tien Tran, D. Lakew, D. Hua, Quang Tuan Do, Nhu-Ngoc Dao, Sungrae Cho

引用次数: 0

Development of MIMO Scheme-based Optical Camera Communication System using Deep Learning method 基于MIMO方案的光学摄像机通信系统的深度学习开发

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067044

Van Linh Nguyen, Duc Hoang Tran, Huy Nguyen, ByungDeok Chung, Y. Jang

引用次数: 0

Preventive Maintenance Techniques through Learning-based Remaining Useful Lifetime Prediction in IoT Sensor Networks: The Survey 物联网传感器网络中基于学习的剩余使用寿命预测的预防性维护技术:调查

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067003

Donghyun Lee, Yong-Kyu Jeon, Junsuk Oh, Chunghyun Lee, Taeyun Ha, Sungrae Cho

引用次数: 0

Digital Twin and Ontology based DDoS Attack Detection in a Smart-Factory 4.0 基于数字孪生和本体的智能工厂4.0 DDoS攻击检测

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067049

Venkata Vivek Gowripeddi, G. Sasirekha, Jyotsna L. Bapat, D. Das

{"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}

引用次数: 1

A New Approach to Lidar and Camera Fusion for Autonomous Driving 自动驾驶激光雷达与摄像头融合的新方法

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10066963

Seunghwan Bae, Dongun Han, Seongkeun Park

{"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}

引用次数: 0

A Mobile Application for Obesity Early Diagnosis Using CNN-based Thermogram Classification 基于cnn热图分类的肥胖症早期诊断移动应用

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10066987

Hendrik Leo, Khairun Saddami, Roslidar, R. Muharar, K. Munadi, F. Arnia

引用次数: 0

FLB2: Layer 2 Blockchain Implementation Scheme on Federated Learning Technique FLB2:基于联邦学习技术的第二层区块链实现方案

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067038

Revin Naufal Alief, Made Adi Paramartha Putra, Augustin Gohil, Jae-Min Lee, Dong‐Seong Kim

{"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}

引用次数: 1

Learning the Protein Language Model of SARS-CoV-2 Spike Proteins SARS-CoV-2刺突蛋白的蛋白质语言模型研究

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067040

Paul Vincent Llanes, Geoffrey A. Solano, Marc Jermaine Pontiveros

{"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}

引用次数: 0

Channel Access Control Instead of Random Backoff Algorithm 信道访问控制代替随机退避算法

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2023-02-20 DOI: 10.1109/ICAIIC57133.2023.10067055

Takashi Imanaka, M. Ohta, M. Taromaru

引用次数: 0