Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067124
Sangwon Oh, Hyeju Shin, Minsoo Hahn, Jinsul Kim
With the emergence of a flexible mix of private and public clouds based on business requirements, the need for a system that supports application deployment to a variety of cloud environments has emerged. In particular, it is necessary to secure the security of data in applications based on federated learning and to monitor resource usage in the cloud. This paper seeks ways to monitor and manage cloud resource usage according to various hyperparameters when conducting federated learning in a hybrid cloud environment. In a Docker-based cloud environment, we present an improved method for using efficient cloud resources while controlling the metric and resource usage trend of the federated learning model according to the imbalance of the data set.
{"title":"Analysis of Resource Usage Management Plan for Federated Learning in Hybrid Cloud","authors":"Sangwon Oh, Hyeju Shin, Minsoo Hahn, Jinsul Kim","doi":"10.1109/ICAIIC57133.2023.10067124","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067124","url":null,"abstract":"With the emergence of a flexible mix of private and public clouds based on business requirements, the need for a system that supports application deployment to a variety of cloud environments has emerged. In particular, it is necessary to secure the security of data in applications based on federated learning and to monitor resource usage in the cloud. This paper seeks ways to monitor and manage cloud resource usage according to various hyperparameters when conducting federated learning in a hybrid cloud environment. In a Docker-based cloud environment, we present an improved method for using efficient cloud resources while controlling the metric and resource usage trend of the federated learning model according to the imbalance of the data set.","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":"125011630","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/ICAIIC57133.2023.10067075
H. Honda, Phuong Dinh, Pham Thu Thao, Yuho Tabata, Bui Duc Anh
A novel non-cooperative game theory-based approach for dimensionality reduction is proposed. We regard the sample elements in a higher-dimensional space as players in a game each of which has its strategy. A set of these strategies was implemented as an embedding of dimensionality reduction, which maps the sample elements into lower-dimensional spaces. Based on the theory of non-cooperative $N$-player games, we show the existence of Nash equilibria. We also provide an algorithm that yields Nash equilibrium based on the theory of nonlinear functional analysis.
{"title":"Dimensionality reduction as a non-cooperative game","authors":"H. Honda, Phuong Dinh, Pham Thu Thao, Yuho Tabata, Bui Duc Anh","doi":"10.1109/ICAIIC57133.2023.10067075","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067075","url":null,"abstract":"A novel non-cooperative game theory-based approach for dimensionality reduction is proposed. We regard the sample elements in a higher-dimensional space as players in a game each of which has its strategy. A set of these strategies was implemented as an embedding of dimensionality reduction, which maps the sample elements into lower-dimensional spaces. Based on the theory of non-cooperative $N$-player games, we show the existence of Nash equilibria. We also provide an algorithm that yields Nash equilibrium based on the theory of nonlinear functional analysis.","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":"123414365","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/ICAIIC57133.2023.10067101
M. Nur, Hazriani, N. K. Nur
This study aims to implement the genetic algorithm by testing the appropriate crossover methods in order to obtain optimal disaster evacuation routes based three main indicators, namely travel time, possible transportation mode, and affected road conditions. The research phase begins with establishing a flood-affected area scenario consisting of the victim's initial location, evacuation location, routing areas, affected road conditions, distance, as well as travel time. The genetic algorithm is applied by representing the genes and chromosomes based on the available data, generating the initial population and calculating the fitness value. At the stage of determining the parent in forming a new individual, roulette wheel selection is used. For the crossover method to produce new individuals, there are 3 methods tested namely single-point, two-point and uniform crossover. The new formed individuals are then mutated with a probability level of 0.1. The last stage is to form a new population by sorting individuals with the highest fitness value. These processes took place with an iteration limit of 1000. Based on the results of the implementation and tests conducted, the uniform crossover method has the most optimal results with accuracy 90% and highest fitness value of 0.896. Meanwhile, the two others methods two-point and single-point have extremely lower accuracy which are 70% and 60% respectively. This result confirmed the statement of previous research which convinced that the uniform crossover is the most effective crossover method.
{"title":"Crossover Methods Comparison in Flood Evacuation Route Optimization","authors":"M. Nur, Hazriani, N. K. Nur","doi":"10.1109/ICAIIC57133.2023.10067101","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067101","url":null,"abstract":"This study aims to implement the genetic algorithm by testing the appropriate crossover methods in order to obtain optimal disaster evacuation routes based three main indicators, namely travel time, possible transportation mode, and affected road conditions. The research phase begins with establishing a flood-affected area scenario consisting of the victim's initial location, evacuation location, routing areas, affected road conditions, distance, as well as travel time. The genetic algorithm is applied by representing the genes and chromosomes based on the available data, generating the initial population and calculating the fitness value. At the stage of determining the parent in forming a new individual, roulette wheel selection is used. For the crossover method to produce new individuals, there are 3 methods tested namely single-point, two-point and uniform crossover. The new formed individuals are then mutated with a probability level of 0.1. The last stage is to form a new population by sorting individuals with the highest fitness value. These processes took place with an iteration limit of 1000. Based on the results of the implementation and tests conducted, the uniform crossover method has the most optimal results with accuracy 90% and highest fitness value of 0.896. Meanwhile, the two others methods two-point and single-point have extremely lower accuracy which are 70% and 60% respectively. This result confirmed the statement of previous research which convinced that the uniform crossover is the most effective crossover method.","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":"127659054","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/ICAIIC57133.2023.10067056
D. Lakew, Anh-Tien Tran, Arooj Masood, Nhu-Ngoc Dao, Sungrae Cho
Aerial access networks (AANs) consisting of low altitude platforms (LAPs) and high altitude platforms (HAPs) have been considered as emerging wireless networking technologies to enhance both the capacity and coverage of future wireless networks, especially in remote and hard to reach areas with lack of terrestrial base stations. However, the limited onboard resources and high dynamicity of the network make challenging to optimally manage both the communication and computation resources for an efficient aerial networking infrastructure. On the other hand, artificial intelligence (AI), especially reinforcement learning- and deep reinforcement learning-based networking, are attracting significant attention to capture the network dynamicity and long-term resource management performance, recently. Thus, in this paper, we first provide a taxonomy of AI-driven aerial access networks and then, present a review and discussion on the state-of-the-art researches on AI-driven AANs from the communication and computation perspective. Moreover, we identify existing research challenges and provide future research direction for further investigations.
{"title":"A Review on AI-Driven Aerial Access Networks: Challenges and Open Research Issues","authors":"D. Lakew, Anh-Tien Tran, Arooj Masood, Nhu-Ngoc Dao, Sungrae Cho","doi":"10.1109/ICAIIC57133.2023.10067056","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067056","url":null,"abstract":"Aerial access networks (AANs) consisting of low altitude platforms (LAPs) and high altitude platforms (HAPs) have been considered as emerging wireless networking technologies to enhance both the capacity and coverage of future wireless networks, especially in remote and hard to reach areas with lack of terrestrial base stations. However, the limited onboard resources and high dynamicity of the network make challenging to optimally manage both the communication and computation resources for an efficient aerial networking infrastructure. On the other hand, artificial intelligence (AI), especially reinforcement learning- and deep reinforcement learning-based networking, are attracting significant attention to capture the network dynamicity and long-term resource management performance, recently. Thus, in this paper, we first provide a taxonomy of AI-driven aerial access networks and then, present a review and discussion on the state-of-the-art researches on AI-driven AANs from the communication and computation perspective. Moreover, we identify existing research challenges and provide future research direction for further investigations.","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":"127891959","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/ICAIIC57133.2023.10066985
D. M. S. Bhatti, Haewoon Nam
Federated learning is a novel approach of training the global model on the server by utilizing the personal data of the end users while data privacy is preserved. The users called clients are required to perform the local training using their local datasets and forward those trained local models to the server, in which the local models are aggregated to update the global model. This process of global training is carried out for several rounds until the convergence. Practically, the clients' data is non-independent and identically distributed (Non-IID). Hence, the updated local model of each client may vary from every other client due to heterogeneity among them. Hence, the process of aggregating the diversified local models of clients has a huge impact on the performance of global training. This article proposes a performance efficient aggregation approach for federated learning, which considers the data heterogeneity among clients before aggregating the received local models. The proposed approach is compared with the conventional federated learning methods, and it achieves improved performance.
{"title":"A Performance Efficient Approach of Global Training in Federated Learning","authors":"D. M. S. Bhatti, Haewoon Nam","doi":"10.1109/ICAIIC57133.2023.10066985","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066985","url":null,"abstract":"Federated learning is a novel approach of training the global model on the server by utilizing the personal data of the end users while data privacy is preserved. The users called clients are required to perform the local training using their local datasets and forward those trained local models to the server, in which the local models are aggregated to update the global model. This process of global training is carried out for several rounds until the convergence. Practically, the clients' data is non-independent and identically distributed (Non-IID). Hence, the updated local model of each client may vary from every other client due to heterogeneity among them. Hence, the process of aggregating the diversified local models of clients has a huge impact on the performance of global training. This article proposes a performance efficient aggregation approach for federated learning, which considers the data heterogeneity among clients before aggregating the received local models. The proposed approach is compared with the conventional federated learning methods, and it achieves improved performance.","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":"121142592","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/ICAIIC57133.2023.10067006
J. Tsiligaridis
An ensemble method is viewed as a compound model. The purpose of such a model is to achieve better predictive performance. The attempt is to tune predictions to observations by decreasing model variance, and bias. First the work focuses at the presentation of the Projective Decision Tree Algorithm (PA), a sort of Decision Tree (DT) based on purity and using the criterion of next node (CNN). Secondly, two sets of algorithms that provide improvement of the predictive performance are developed the first set of the Tree-Based Ensemble models of bagging and boosting types and the second set of known individual algorithms. The accuracy performance of the two sets with comparison is examined. Promising results based on accuracy of the proposed models are obtained.
{"title":"Tree-Based Ensemble Models and Algorithms for Classification","authors":"J. Tsiligaridis","doi":"10.1109/ICAIIC57133.2023.10067006","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067006","url":null,"abstract":"An ensemble method is viewed as a compound model. The purpose of such a model is to achieve better predictive performance. The attempt is to tune predictions to observations by decreasing model variance, and bias. First the work focuses at the presentation of the Projective Decision Tree Algorithm (PA), a sort of Decision Tree (DT) based on purity and using the criterion of next node (CNN). Secondly, two sets of algorithms that provide improvement of the predictive performance are developed the first set of the Tree-Based Ensemble models of bagging and boosting types and the second set of known individual algorithms. The accuracy performance of the two sets with comparison is examined. Promising results based on accuracy of the proposed models are obtained.","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":"115964431","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/ICAIIC57133.2023.10066972
Sunghoon Hong, Dae-Geun Park
Convolutional neural networks with powerful visual image analysis for artificial intelligence are gaining popularity in many research fields, leading to the development of various high-performance algorithms for convolution operators present in these networks. One of these approaches is implemented with general matrix multiplication (GEMM) using the well-known im2col transform for fast convolution operations. In this paper, we propose a multi-core processor-based convolution technique for high-speed convolutional neural networks (CNNs) using differential images. The proposed method improves the convolutional layer's response speed by reducing the computational complexity and using multi-thread technology. In addition, the proposed algorithm has the advantage of being compatible with all types of CNNs. We use the darknet network to evaluate the convolutional layer's performance and show the best performance of the proposed algorithm when using 4-thread parallel processing.
{"title":"Differential Image-based Fast and Compatible Convolutional Layers for Multi-core Processors","authors":"Sunghoon Hong, Dae-Geun Park","doi":"10.1109/ICAIIC57133.2023.10066972","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066972","url":null,"abstract":"Convolutional neural networks with powerful visual image analysis for artificial intelligence are gaining popularity in many research fields, leading to the development of various high-performance algorithms for convolution operators present in these networks. One of these approaches is implemented with general matrix multiplication (GEMM) using the well-known im2col transform for fast convolution operations. In this paper, we propose a multi-core processor-based convolution technique for high-speed convolutional neural networks (CNNs) using differential images. The proposed method improves the convolutional layer's response speed by reducing the computational complexity and using multi-thread technology. In addition, the proposed algorithm has the advantage of being compatible with all types of CNNs. We use the darknet network to evaluate the convolutional layer's performance and show the best performance of the proposed algorithm when using 4-thread parallel processing.","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":"126403266","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/ICAIIC57133.2023.10067092
R. Lackes, J. Sengewald
Predicting the total manufacturing costs of a new product early in its development is an obstacle for many businesses, especially when selecting between different product designs and their cost implications. Typically, material costs comprise a large part of total manufacturing costs, and therefore obtaining an early estimate of material costs can help businesses in predicting the total manufacturing costs more accurately. At the early stage of product development, with many imponderables and frequent design modifications, it would be impractical to obtain quotations from suppliers. We, therefore, developed a two-stage machine learning scheme estimating the material cost to guide alternative product design choices that yield a lower total manufacturing cost. Our innovative two-stage technique for cost estimation is meant to overcome this issue. In this paper, we demonstrate that neural networks, a prevalent technique in the literature, can be enhanced by adding the concept of modularity to the estimation of the pricing of technical components already during the design process of a new product.
{"title":"Early Product Cost Estimation by Intelligent Machine Learning Algorithms","authors":"R. Lackes, J. Sengewald","doi":"10.1109/ICAIIC57133.2023.10067092","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067092","url":null,"abstract":"Predicting the total manufacturing costs of a new product early in its development is an obstacle for many businesses, especially when selecting between different product designs and their cost implications. Typically, material costs comprise a large part of total manufacturing costs, and therefore obtaining an early estimate of material costs can help businesses in predicting the total manufacturing costs more accurately. At the early stage of product development, with many imponderables and frequent design modifications, it would be impractical to obtain quotations from suppliers. We, therefore, developed a two-stage machine learning scheme estimating the material cost to guide alternative product design choices that yield a lower total manufacturing cost. Our innovative two-stage technique for cost estimation is meant to overcome this issue. In this paper, we demonstrate that neural networks, a prevalent technique in the literature, can be enhanced by adding the concept of modularity to the estimation of the pricing of technical components already during the design process of a new product.","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":"126404925","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/ICAIIC57133.2023.10067052
Tri-Hai Nguyen, Heejae Park, Laihyuk Park
Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) technologies have recently been identified as enablers for future wireless networks. Deep reinforcement learning (DRL) is also a potential technique for optimizing performance in dynamic and complex networking environments. In this paper, we examine the state-of-the-art studies on DRL utilization in RIS-UAV communication systems concerning their objectives, optimization parameters, deployment scenarios, and DRL methods. In addition, we emphasize research challenges and directions that can be addressed to improve RIS-UAV networks.
{"title":"Recent Studies on Deep Reinforcement Learning in RIS-UAV Communication Networks","authors":"Tri-Hai Nguyen, Heejae Park, Laihyuk Park","doi":"10.1109/ICAIIC57133.2023.10067052","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067052","url":null,"abstract":"Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) technologies have recently been identified as enablers for future wireless networks. Deep reinforcement learning (DRL) is also a potential technique for optimizing performance in dynamic and complex networking environments. In this paper, we examine the state-of-the-art studies on DRL utilization in RIS-UAV communication systems concerning their objectives, optimization parameters, deployment scenarios, and DRL methods. In addition, we emphasize research challenges and directions that can be addressed to improve RIS-UAV 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":"129394519","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/ICAIIC57133.2023.10066966
Rifqi Abdillah, R. Sarno, T. Amri, Faris Atoil Haq, K. R. Sungkono, Dwi Sunaryono
This paper aims to evaluate Photoplethysmography signals taken using fingertip pulse waves on human fingers which are generally not all in good condition. In common devices, the data obtained does not only contain photoplethysmography signals, but some noise also contaminates it. Noise is an unwanted ripple-shaped signal existing in signal transmission. Noise will interfere the desired quality of the received signal and ultimately change the information contained in the signal. This situation requires improvements to the photoplethysmography signal to make the signals are in the best condition so machine learning produces a more optimal output. Noise filters cannot be done with the same treatment because noise level in each data is different and must have different filter weights. This paper proposes a method to filter noise based on the level of noise in the signal. The approach taken in this study uses two stages, clustering and noise filtering. The first approach is clustering using the K-means clustering method by utilizing the coefficient of variation and slope features to group signals based on their noise level. The second approach uses exponential filtering, which performs by weighting the filter based on the cluster so that the data have different adjustments ratio of the level of smoothing. The result of the signal-to-noise ratio on Non-filtered Data is 181.49. Signal to noise ratio on the Constant Weighted Filter is 183.79 and increases to 187.48 after using the Clustered and Weighted Filter method.
{"title":"Clustering of Photoplethysmography Data Signals for Developing Noise Filters","authors":"Rifqi Abdillah, R. Sarno, T. Amri, Faris Atoil Haq, K. R. Sungkono, Dwi Sunaryono","doi":"10.1109/ICAIIC57133.2023.10066966","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066966","url":null,"abstract":"This paper aims to evaluate Photoplethysmography signals taken using fingertip pulse waves on human fingers which are generally not all in good condition. In common devices, the data obtained does not only contain photoplethysmography signals, but some noise also contaminates it. Noise is an unwanted ripple-shaped signal existing in signal transmission. Noise will interfere the desired quality of the received signal and ultimately change the information contained in the signal. This situation requires improvements to the photoplethysmography signal to make the signals are in the best condition so machine learning produces a more optimal output. Noise filters cannot be done with the same treatment because noise level in each data is different and must have different filter weights. This paper proposes a method to filter noise based on the level of noise in the signal. The approach taken in this study uses two stages, clustering and noise filtering. The first approach is clustering using the K-means clustering method by utilizing the coefficient of variation and slope features to group signals based on their noise level. The second approach uses exponential filtering, which performs by weighting the filter based on the cluster so that the data have different adjustments ratio of the level of smoothing. The result of the signal-to-noise ratio on Non-filtered Data is 181.49. Signal to noise ratio on the Constant Weighted Filter is 183.79 and increases to 187.48 after using the Clustered and Weighted Filter method.","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":"131133705","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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