Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067106
Bagas Tri Wibowo, Dade Nurjanah, Hani Nurrahmi
Misogyny is a behavior that hates or dislikes women Text classification can be used to identify misogyny text. One text classification method currently popular and proven to have good performance is the Bidirectional Encoder From Transformers (BERT). Fine-tuning is a method to transfer knowledge from a trained model to a new model to complete a new task. This study focuses on building a misogyny identification model with IndoBert pre-trained model provided by IndoNLU. The identification of Misogyny model obtained the best results with an accuracy value of 83.74% and by using K-fold cross-validation, the average validation value is 77.86%.
{"title":"Identification of Misogyny on Social Media in Indonesian Using Bidirectional Encoder Representations From Transformers (BERT)","authors":"Bagas Tri Wibowo, Dade Nurjanah, Hani Nurrahmi","doi":"10.1109/ICAIIC57133.2023.10067106","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067106","url":null,"abstract":"Misogyny is a behavior that hates or dislikes women Text classification can be used to identify misogyny text. One text classification method currently popular and proven to have good performance is the Bidirectional Encoder From Transformers (BERT). Fine-tuning is a method to transfer knowledge from a trained model to a new model to complete a new task. This study focuses on building a misogyny identification model with IndoBert pre-trained model provided by IndoNLU. The identification of Misogyny model obtained the best results with an accuracy value of 83.74% and by using K-fold cross-validation, the average validation value is 77.86%.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127897659","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/ICAIIC57133.2023.10067105
Chang Woo Choi, Hyo-eun Kang, Yoonyoung Hong, Yong Su Kim, Guem Bo Kim, Aji Teguh Prihatno, Jang Hyun Ji, Seungdo Hong, Ho Won Kim
It is essential to perform flow analysis in all spaces where people live. For example, designing the shape of the wing by analyzing the flow flowing through the wing of an airplane, or finding an appropriate air conditioner installation location by analyzing the flow according to the location of the air conditioner in the indoor space. In this study, we propose a deep learning model that performs real-time flow analysis assuming an indoor space that is relatively smaller than outdoor space. Computational Fluid Dynamics (CFD), a traditional method used for flow analysis, is not suitable for this task because it takes a long time to derive simulation results. Thus, the application of deep learning to flow analysis is considered in the present study because deep learning technology for physics, i.e., fluid mechanics and thermodynamics, can be applied to real spaces. We have constructed a deep learning model based on the TransUnet model that can learn data relationships and capture spatial information. Unlike the existing TransUnet model, our model contains a dense layer to reflect operating and spatial information. train and test data were collected using the ANSYS FLUENT commercial program. On 11 test data cases, the average R2 score between the actual and predicted value was 0.884, and the RMSE was 0.047, which are significant results. We used the image of the entire space as well as a cross-section to see how similar the predicted values were to the actual ones, Although a slight error occurred inside the space, It was confirmed that the flow tendency was accurately learned under the given operating conditions. Flow analysis through simulation based on existing numerical analysis methods requires a minimum of 8 hours for processing. However, our proposed deep learning model significantly reduces the time cost of flow analysis as it requires less than 3 seconds.
{"title":"Indoor Space Flow Analysis Based on Deep Learning","authors":"Chang Woo Choi, Hyo-eun Kang, Yoonyoung Hong, Yong Su Kim, Guem Bo Kim, Aji Teguh Prihatno, Jang Hyun Ji, Seungdo Hong, Ho Won Kim","doi":"10.1109/ICAIIC57133.2023.10067105","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067105","url":null,"abstract":"It is essential to perform flow analysis in all spaces where people live. For example, designing the shape of the wing by analyzing the flow flowing through the wing of an airplane, or finding an appropriate air conditioner installation location by analyzing the flow according to the location of the air conditioner in the indoor space. In this study, we propose a deep learning model that performs real-time flow analysis assuming an indoor space that is relatively smaller than outdoor space. Computational Fluid Dynamics (CFD), a traditional method used for flow analysis, is not suitable for this task because it takes a long time to derive simulation results. Thus, the application of deep learning to flow analysis is considered in the present study because deep learning technology for physics, i.e., fluid mechanics and thermodynamics, can be applied to real spaces. We have constructed a deep learning model based on the TransUnet model that can learn data relationships and capture spatial information. Unlike the existing TransUnet model, our model contains a dense layer to reflect operating and spatial information. train and test data were collected using the ANSYS FLUENT commercial program. On 11 test data cases, the average R2 score between the actual and predicted value was 0.884, and the RMSE was 0.047, which are significant results. We used the image of the entire space as well as a cross-section to see how similar the predicted values were to the actual ones, Although a slight error occurred inside the space, It was confirmed that the flow tendency was accurately learned under the given operating conditions. Flow analysis through simulation based on existing numerical analysis methods requires a minimum of 8 hours for processing. However, our proposed deep learning model significantly reduces the time cost of flow analysis as it requires less than 3 seconds.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"1996 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128204180","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/ICAIIC57133.2023.10066988
Seungchan Woo, Jong-Hyouk Lee
Modern developers typically run their workloads through cloud-native environments such as Docker and Kubernetes. Docker is a platform that runs and manages containers. With the birth of Docker, interest in containers and technology has grown. As one of the container orchestration tools that control and manage containers running on multiple hosts, Kubernetes has a very large share and is used by many cloud companies, making it the standard for practical container orchestration tools. Therefore, in this paper, by analyzing the Kubernetes event timeline, we present the future direction of Kubernetes and Docker, which are key tools in the cloud-native environment.
{"title":"Proposal of Docker and Kubernetes Direction through the Event Timeline of Kubernetes","authors":"Seungchan Woo, Jong-Hyouk Lee","doi":"10.1109/ICAIIC57133.2023.10066988","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066988","url":null,"abstract":"Modern developers typically run their workloads through cloud-native environments such as Docker and Kubernetes. Docker is a platform that runs and manages containers. With the birth of Docker, interest in containers and technology has grown. As one of the container orchestration tools that control and manage containers running on multiple hosts, Kubernetes has a very large share and is used by many cloud companies, making it the standard for practical container orchestration tools. Therefore, in this paper, by analyzing the Kubernetes event timeline, we present the future direction of Kubernetes and Docker, which are key tools in the cloud-native environment.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126285019","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/ICAIIC57133.2023.10066960
Carlos Lua, Ye Zhang, Omar Hekal, Daniel Onwuchekwa, R. Obermaisser
Research on graph neural networks (GNNs) has increasingly gained popularity recently. GNN is considered a powerful tool for solving machine learning tasks that require dealing with irregular topologies such as graph data. Meanwhile, solving the scheduling problems for time-triggered systems has been debated for a long time. Even though several algorithms were proposed to solve this problem, none considered exploiting GNN partially or wholly, solving time-triggered scheduling. In this work, we propose an approach for dynamic adaptation in time-triggered systems using GNN. We use GNNs to solve scheduling problems for time-triggered systems by transforming job allocation probelms to link prediction tasks. The preliminary results show that GNNs have a promising potential to perform job allocation problems in time-triggered systems.
{"title":"GNN Link Prediction for Time-Triggered Systems","authors":"Carlos Lua, Ye Zhang, Omar Hekal, Daniel Onwuchekwa, R. Obermaisser","doi":"10.1109/ICAIIC57133.2023.10066960","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066960","url":null,"abstract":"Research on graph neural networks (GNNs) has increasingly gained popularity recently. GNN is considered a powerful tool for solving machine learning tasks that require dealing with irregular topologies such as graph data. Meanwhile, solving the scheduling problems for time-triggered systems has been debated for a long time. Even though several algorithms were proposed to solve this problem, none considered exploiting GNN partially or wholly, solving time-triggered scheduling. In this work, we propose an approach for dynamic adaptation in time-triggered systems using GNN. We use GNNs to solve scheduling problems for time-triggered systems by transforming job allocation probelms to link prediction tasks. The preliminary results show that GNNs have a promising potential to perform job allocation problems in time-triggered systems.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127080682","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/ICAIIC57133.2023.10067029
Jeonghun Park, Heetae Jin, Jaehan Joo, Geonho Choi, Suk Chan Kim
In the fifth-generation (5G) network, mmWave has been utilized to cope with a demand for an extremely high data rate. However, the harsh propagation characteristic of mmWave signal limits networks' coverage, thus requiring network densification. Under this circumstance, 3GPP has introduced Integrated Access and Backhaul (IAB) architecture for cost-effective network deployment&operation. Contrary to traditional network architecture using wired backhaul links, IAB uses wireless backhaul links to forward data traffic. This feature improves spectrum utilization and cost efficiency. However, due to the dynamic, time-varying environment of the IAB network, finding a proper resource allocation strategy is a challenging issue. In this paper, we formulate the backhaul spectrum allocation problem maximizing user sum capacity. Then propose a double deep Q-Iearning-based backhaul spectrum allocation strategy. The simulation result shows that the proposed reinforcement learning-based spectrum allocation can achieve 20% higher user sum capacity than static rule-based spectrum allocation.
{"title":"Double Deep Q-Learning based Backhaul Spectrum Allocation in Integrated Access and Backhaul Network","authors":"Jeonghun Park, Heetae Jin, Jaehan Joo, Geonho Choi, Suk Chan Kim","doi":"10.1109/ICAIIC57133.2023.10067029","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067029","url":null,"abstract":"In the fifth-generation (5G) network, mmWave has been utilized to cope with a demand for an extremely high data rate. However, the harsh propagation characteristic of mmWave signal limits networks' coverage, thus requiring network densification. Under this circumstance, 3GPP has introduced Integrated Access and Backhaul (IAB) architecture for cost-effective network deployment&operation. Contrary to traditional network architecture using wired backhaul links, IAB uses wireless backhaul links to forward data traffic. This feature improves spectrum utilization and cost efficiency. However, due to the dynamic, time-varying environment of the IAB network, finding a proper resource allocation strategy is a challenging issue. In this paper, we formulate the backhaul spectrum allocation problem maximizing user sum capacity. Then propose a double deep Q-Iearning-based backhaul spectrum allocation strategy. The simulation result shows that the proposed reinforcement learning-based spectrum allocation can achieve 20% higher user sum capacity than static rule-based spectrum allocation.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127538583","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/ICAIIC57133.2023.10067062
Van Toan Quyen, Jong Hyuk Lee, Min Young Kim
Semantic segmentation is a complicated topic when they require strictly the object boundary accuracy. For autonomous driving applications, they have to face a long range of objective sizes in the street scenes, so a single field of views is not suitable to extract input features. Feature pyramid network (FPN) is an effective method for computer vision tasks such as object detection and semantic segmentation. The architecture of this approach composes of a bottom-up pathway and a top-down pathway. Based on the structure, we can obtain rich spatial information from the largest layer and extract rich segmentation information from lower-scale features. The traditional FPN efficiently captures different objective sizes by using multiple receptive fields and then predicts the outputs from the concatenated features. The final feature combination is not optimistic when they burden the hardware with huge computation and reduce the semantic information. In this paper, we propose multiple predictions for semantic segmentation. Instead of combining four-feature scales together, the proposed method processes separately three lower scales as the contextual contributor and the largest features as the coarser-information branch. Each contextual feature is concatenated with the coarse branch to generate an individual prediction. By deploying this architecture, a single prediction effectively segments specific objective sizes. Finally, score maps are fused together in order to gather the prominent weights from the different predictions. A series of experiments is implemented to validate the efficiency on various open data sets. We have achieved good results 76.4% $m$IoU at 52 FPS on Cityscapes and 43.6% $m$IoU on Mapillary Vistas.
{"title":"Enhanced-feature pyramid network for semantic segmentation","authors":"Van Toan Quyen, Jong Hyuk Lee, Min Young Kim","doi":"10.1109/ICAIIC57133.2023.10067062","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067062","url":null,"abstract":"Semantic segmentation is a complicated topic when they require strictly the object boundary accuracy. For autonomous driving applications, they have to face a long range of objective sizes in the street scenes, so a single field of views is not suitable to extract input features. Feature pyramid network (FPN) is an effective method for computer vision tasks such as object detection and semantic segmentation. The architecture of this approach composes of a bottom-up pathway and a top-down pathway. Based on the structure, we can obtain rich spatial information from the largest layer and extract rich segmentation information from lower-scale features. The traditional FPN efficiently captures different objective sizes by using multiple receptive fields and then predicts the outputs from the concatenated features. The final feature combination is not optimistic when they burden the hardware with huge computation and reduce the semantic information. In this paper, we propose multiple predictions for semantic segmentation. Instead of combining four-feature scales together, the proposed method processes separately three lower scales as the contextual contributor and the largest features as the coarser-information branch. Each contextual feature is concatenated with the coarse branch to generate an individual prediction. By deploying this architecture, a single prediction effectively segments specific objective sizes. Finally, score maps are fused together in order to gather the prominent weights from the different predictions. A series of experiments is implemented to validate the efficiency on various open data sets. We have achieved good results 76.4% $m$IoU at 52 FPS on Cityscapes and 43.6% $m$IoU on Mapillary Vistas.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127022946","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/ICAIIC57133.2023.10067126
Yafeng Deng, Young-June Choi
Many efforts have been done to increase the performance of vehicle-to-vehicle (V2V) services, such as basic safety message (BSM) and collision avoidance warning. However, high dynamics, such as topology and channel condition, still pose big challenges to resource allocation tasks in vehicular networks. A previous work, relative distance based MAC [1], is proposed to address merging collision. The dynamics can not be fully addressed because thresholds are used. Therefore, we intuitively adapt a dueling deep Q-network [2] to tune the threshold based on the aforementioned work to further address merging collision. The simulation results demonstrate the improvement of the proposed algorithm.
{"title":"A Reinforcement Learning Assisted Relative Distance based MAC in Vehicular Networks","authors":"Yafeng Deng, Young-June Choi","doi":"10.1109/ICAIIC57133.2023.10067126","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067126","url":null,"abstract":"Many efforts have been done to increase the performance of vehicle-to-vehicle (V2V) services, such as basic safety message (BSM) and collision avoidance warning. However, high dynamics, such as topology and channel condition, still pose big challenges to resource allocation tasks in vehicular networks. A previous work, relative distance based MAC [1], is proposed to address merging collision. The dynamics can not be fully addressed because thresholds are used. Therefore, we intuitively adapt a dueling deep Q-network [2] to tune the threshold based on the aforementioned work to further address merging collision. The simulation results demonstrate the improvement of the proposed algorithm.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131388761","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/ICAIIC57133.2023.10067013
Mrinmoy Sarker Turja, Tae-Ho Kwon, Hyoungkeun Kim, Ki-Doo Kim
Diabetes has recently become a more serious disease. Almost every family has at least one diabetic. Patients have to regularly monitor their blood glucose levels, and using an invasive device on the other hand can be really painful and less reliable. This is because blood glucose levels fluctuate more with food intake. On the contrary, HbA1c level does not fluctuate as much as that of blood glucose. Therefore, in this study, XGBoost calibration considering only important features for Monte-Carlo simulation based noninvasive HbA1c estimation with PPG signals was proposed. After considering the important 13 of the 45 features, the model achieved a Pearson's r value of 98.90%.
{"title":"XGBoost Calibration Considering Feature Importance for Noninvasive HbA1c Estimation Using PPG Signals","authors":"Mrinmoy Sarker Turja, Tae-Ho Kwon, Hyoungkeun Kim, Ki-Doo Kim","doi":"10.1109/ICAIIC57133.2023.10067013","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067013","url":null,"abstract":"Diabetes has recently become a more serious disease. Almost every family has at least one diabetic. Patients have to regularly monitor their blood glucose levels, and using an invasive device on the other hand can be really painful and less reliable. This is because blood glucose levels fluctuate more with food intake. On the contrary, HbA1c level does not fluctuate as much as that of blood glucose. Therefore, in this study, XGBoost calibration considering only important features for Monte-Carlo simulation based noninvasive HbA1c estimation with PPG signals was proposed. After considering the important 13 of the 45 features, the model achieved a Pearson's r value of 98.90%.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124425211","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/ICAIIC57133.2023.10067066
Yeoun Chan Kim, Pankaj Agarwal
Knowledge tracing and learning path optimization are active research fields in education with AI technologies. The purpose of knowledge tracing is to model student's knowledge state of a concept and to predict the percentage of correctly answer a next question. Using the technology of modeling a student's knowledge state, learning path optimization technologies recommend personalized learning path for efficient learning. These two research fields are implemented on learning management systems for individual learning. In this research paper, method of using knowledge tracing and learning path optimization in group learning environment is suggested. Group score prediction model predicts number of students who will answer their next question correctly by utilizing one-dimensional convolution neural network and fully connected layers. The model is adopted in a group score prediction system where instructors utilize the model's output to create a question set corresponding to their strategy and students' responses are used to re-train and evaluate the model.
{"title":"AI in Classroom: Group Score Prediction System","authors":"Yeoun Chan Kim, Pankaj Agarwal","doi":"10.1109/ICAIIC57133.2023.10067066","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067066","url":null,"abstract":"Knowledge tracing and learning path optimization are active research fields in education with AI technologies. The purpose of knowledge tracing is to model student's knowledge state of a concept and to predict the percentage of correctly answer a next question. Using the technology of modeling a student's knowledge state, learning path optimization technologies recommend personalized learning path for efficient learning. These two research fields are implemented on learning management systems for individual learning. In this research paper, method of using knowledge tracing and learning path optimization in group learning environment is suggested. Group score prediction model predicts number of students who will answer their next question correctly by utilizing one-dimensional convolution neural network and fully connected layers. The model is adopted in a group score prediction system where instructors utilize the model's output to create a question set corresponding to their strategy and students' responses are used to re-train and evaluate the model.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121450683","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/ICAIIC57133.2023.10066981
Alexander Nurenie, Y. Heryadi, Lukas, W. Suparta, Yulyani Arifin
Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.
{"title":"Predicting Human Activity with LSTM Face Detection on Server Surveillance System","authors":"Alexander Nurenie, Y. Heryadi, Lukas, W. Suparta, Yulyani Arifin","doi":"10.1109/ICAIIC57133.2023.10066981","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066981","url":null,"abstract":"Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128621247","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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