Pub Date : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608825
Siti Syahirah Ibrahim, N. Abdullah
Most of the existing patient medication records used paper card systems to record the medicine prescription in the pharmacy. Different countries use different systems, yet hold the same system workflow. This paper reviews ontology works and their usage in medical applications. As technology evolves, this paper proposes an ontology modeling in developing an electronic patient medication record. This paper uses DOID ontology for its disease and symptom ontology in the proposed methodology. The proposed medication ontology can be embedded in a web application that can be used by patients and doctors. However, the proposed ontology is only specified for the Malaysian environment which covers certain prescribed medicine allowed to be sold at the pharmacy.
{"title":"Ontology Design for Patient Medication Record","authors":"Siti Syahirah Ibrahim, N. Abdullah","doi":"10.1109/ICTS52701.2021.9608825","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608825","url":null,"abstract":"Most of the existing patient medication records used paper card systems to record the medicine prescription in the pharmacy. Different countries use different systems, yet hold the same system workflow. This paper reviews ontology works and their usage in medical applications. As technology evolves, this paper proposes an ontology modeling in developing an electronic patient medication record. This paper uses DOID ontology for its disease and symptom ontology in the proposed methodology. The proposed medication ontology can be embedded in a web application that can be used by patients and doctors. However, the proposed ontology is only specified for the Malaysian environment which covers certain prescribed medicine allowed to be sold at the pharmacy.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"6 1","pages":"84-89"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77773043","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608182
A. S. Shminan, Lee Jun Choi, Mohd Hardyman Barawi, Wan Norizan Wan Hashim, Harisson Andy
Interactive Virtual Academic System Prototype (InVesa) is a conceptual automated system dedicated to students where a theory of personality test, Holland Code (RIASEC) is integrated to aid students in selecting the ideal elective subjects for their Cognitive Science course. With InVesa, rather than giving alerts to academic advisors, students are assigned personalized advice by the system on the recommended elective subjects based on their RIASEC result. A student may choose to accept a list of subjects' recommendations from the system or to retake the RIASEC test until they are satisfied with the suggested subjects provided by the system. In addition, students with less time meeting their academic advisors to consult on their subject enrolment may avoid general issues that are common in the standard appointment registration system, such as multiple registrations and long queues when scheduling an appointment. It can be concluded that the proposed application meets the requirements. The proposed application provides a convenient and efficient solution to gain the required information and will benefit the targeted users, which achieves and fulfills the forth of Sustainable Development Goals (SDG) of Quality Education for better education quality for all in terms of gender, age and status.
{"title":"InVesa 1.0: The Conceptual Framework of Interactive Virtual Academic Advisor System based on Psychological Profiles","authors":"A. S. Shminan, Lee Jun Choi, Mohd Hardyman Barawi, Wan Norizan Wan Hashim, Harisson Andy","doi":"10.1109/ICTS52701.2021.9608182","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608182","url":null,"abstract":"Interactive Virtual Academic System Prototype (InVesa) is a conceptual automated system dedicated to students where a theory of personality test, Holland Code (RIASEC) is integrated to aid students in selecting the ideal elective subjects for their Cognitive Science course. With InVesa, rather than giving alerts to academic advisors, students are assigned personalized advice by the system on the recommended elective subjects based on their RIASEC result. A student may choose to accept a list of subjects' recommendations from the system or to retake the RIASEC test until they are satisfied with the suggested subjects provided by the system. In addition, students with less time meeting their academic advisors to consult on their subject enrolment may avoid general issues that are common in the standard appointment registration system, such as multiple registrations and long queues when scheduling an appointment. It can be concluded that the proposed application meets the requirements. The proposed application provides a convenient and efficient solution to gain the required information and will benefit the targeted users, which achieves and fulfills the forth of Sustainable Development Goals (SDG) of Quality Education for better education quality for all in terms of gender, age and status.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"26 1","pages":"112-117"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82913134","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608629
M. A. Nugroho, Sidik Prabowo, Mas'ud Adhi Saputra, M. Abdurohman
This paper proposes the port knocking method to offload the network functions on Software-Defined Networks to programmable data plane. The main drawback of centralized SDN controller architecture is causing bottlenecks in the network because every packet that arrives at the switch must be forwarded to the controller first. The controller will decide whether the packet is allowed to be forwarded or dropped, resulting in increased processing delay for packet processing. There are several previous methods have been proposed. However, they could not meet the need of network performance. Thus, we decide to migrate the several controller functions in the data plane. This paper presents port knocking (PkoCK) implementation in programmable data plane. PKock successfully offloads the port knocking implementation to the data plane and reduces the processing delay 19% compared to SDN-based port knocking.
{"title":"Port Knocking Implementation on Programmable Data Plane","authors":"M. A. Nugroho, Sidik Prabowo, Mas'ud Adhi Saputra, M. Abdurohman","doi":"10.1109/ICTS52701.2021.9608629","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608629","url":null,"abstract":"This paper proposes the port knocking method to offload the network functions on Software-Defined Networks to programmable data plane. The main drawback of centralized SDN controller architecture is causing bottlenecks in the network because every packet that arrives at the switch must be forwarded to the controller first. The controller will decide whether the packet is allowed to be forwarded or dropped, resulting in increased processing delay for packet processing. There are several previous methods have been proposed. However, they could not meet the need of network performance. Thus, we decide to migrate the several controller functions in the data plane. This paper presents port knocking (PkoCK) implementation in programmable data plane. PKock successfully offloads the port knocking implementation to the data plane and reduces the processing delay 19% compared to SDN-based port knocking.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"26 1","pages":"35-39"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82151824","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608813
Saleh Shahriar, Hasibul Islam Peyal, Md. Nahiduzzaman, Md. Abu Hanif Pramanik
Irrigation is very important fact in the field of agriculture. A machine learning and Internet of Things (IoT) based irrigation system is proposed here to make irrigation process more efficient. Soil moisture and Temperature value are taken by the sensors in Raspberry Pi with the help of analog to digital converter (ADC). Serial peripheral interface (SPI) protocol is used here to do it. A machine learning model is trained with Naïve Bayes algorithm and deployed in Raspberry Pi. The machine learning model controls the irrigation system with the sensor value with almost 98.33% accuracy. A prototype project of this irrigation system is also developed with a water pump and relay to show that how accurately the system works.
{"title":"An IoT-Based Real-Time Intelligent Irrigation System using Machine Learning","authors":"Saleh Shahriar, Hasibul Islam Peyal, Md. Nahiduzzaman, Md. Abu Hanif Pramanik","doi":"10.1109/ICTS52701.2021.9608813","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608813","url":null,"abstract":"Irrigation is very important fact in the field of agriculture. A machine learning and Internet of Things (IoT) based irrigation system is proposed here to make irrigation process more efficient. Soil moisture and Temperature value are taken by the sensors in Raspberry Pi with the help of analog to digital converter (ADC). Serial peripheral interface (SPI) protocol is used here to do it. A machine learning model is trained with Naïve Bayes algorithm and deployed in Raspberry Pi. The machine learning model controls the irrigation system with the sensor value with almost 98.33% accuracy. A prototype project of this irrigation system is also developed with a water pump and relay to show that how accurately the system works.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"19 1","pages":"277-281"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77876836","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608600
Weicong Ma, W. Chao
System description is a term that means an “artifact” created explicitly by humans to model a particular given system. In this article, we propose using Structure-Behavior Coalescence (SBC) process algebra. It applies to the following description of the System: an arrangement of parts or components that together specify how to derive behavior or meaning that the individual constituents do not. Describing the arrangement of parts or components that together specify how to derive behavior or meaning implies integrating a system structure with that System's behavior. However, most conventional system description approaches do not specify the integration between system structure and system behavior. In other words, system structure and system behavior are regarded as separate and always distinct, which becomes/forms the central rationality of inconsistent model difficulties. The SBC system description specifies how to derive behavior or meaning from the arrangement of parts or components, thereby avoiding model inconsistency.
{"title":"Contemporary Concepts, Descriptions and Language of Systems Using SBC Process Algebra","authors":"Weicong Ma, W. Chao","doi":"10.1109/ICTS52701.2021.9608600","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608600","url":null,"abstract":"System description is a term that means an “artifact” created explicitly by humans to model a particular given system. In this article, we propose using Structure-Behavior Coalescence (SBC) process algebra. It applies to the following description of the System: an arrangement of parts or components that together specify how to derive behavior or meaning that the individual constituents do not. Describing the arrangement of parts or components that together specify how to derive behavior or meaning implies integrating a system structure with that System's behavior. However, most conventional system description approaches do not specify the integration between system structure and system behavior. In other words, system structure and system behavior are regarded as separate and always distinct, which becomes/forms the central rationality of inconsistent model difficulties. The SBC system description specifies how to derive behavior or meaning from the arrangement of parts or components, thereby avoiding model inconsistency.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"55 1","pages":"294-300"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76945969","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608436
Mohamad Ishak, Md Shahidur Rahaman, T. Mahmud
Farmers' contribution to the economy and national GDP is ineffable. Even though there has been a significant technological advancement in the field of agricultural crops production and management, farmers in developing countries still follow the traditional methods of farming which at many times leads them a loss. Moreover, they don't know the correct market value of their crops, and distributors befool them with the price and value. On the other hand, in times of price hikes and crisis, due to the proper channel government failed to buy crops from them. This paper aims to analyze the primitive approach of cultivation and develop a model for crop prediction using machine learning and provide a model for proper crops management after production. We propose an intelligent system that can predict the best possible crops only by providing the present location of a farmer, the overall guideline from soil preparation to crop yielding, and the systemic approach of crops marketing from farmer to consumer. We used Random Forest Regression, Support Vector Regression and Voting Regression techniques for crop yield prediction and used the real-time data of climate, weather, and soil for the specific region. On the other hand, the market monitoring system will help for proper pricing of the crops and provide transparency for all the stakeholders related to crop marketing where they can buy and sell their products utilizing our system.
{"title":"FarmEasy: An Intelligent Platform to Empower Crops Prediction and Crops Marketing","authors":"Mohamad Ishak, Md Shahidur Rahaman, T. Mahmud","doi":"10.1109/ICTS52701.2021.9608436","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608436","url":null,"abstract":"Farmers' contribution to the economy and national GDP is ineffable. Even though there has been a significant technological advancement in the field of agricultural crops production and management, farmers in developing countries still follow the traditional methods of farming which at many times leads them a loss. Moreover, they don't know the correct market value of their crops, and distributors befool them with the price and value. On the other hand, in times of price hikes and crisis, due to the proper channel government failed to buy crops from them. This paper aims to analyze the primitive approach of cultivation and develop a model for crop prediction using machine learning and provide a model for proper crops management after production. We propose an intelligent system that can predict the best possible crops only by providing the present location of a farmer, the overall guideline from soil preparation to crop yielding, and the systemic approach of crops marketing from farmer to consumer. We used Random Forest Regression, Support Vector Regression and Voting Regression techniques for crop yield prediction and used the real-time data of climate, weather, and soil for the specific region. On the other hand, the market monitoring system will help for proper pricing of the crops and provide transparency for all the stakeholders related to crop marketing where they can buy and sell their products utilizing our system.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"25 1","pages":"224-229"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73171785","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608235
S. Hidayati, Erliyah Nurul Jannah, Y. Anistyasari
Clothing genre recognition has shown its capabilities in many intelligent fashion scenarios. Given an unannotated consumer photo of a full-body person, the proposed study addresses the problem of recognizing the upperwear genres presented in that photo. Although the topic continues to show progress, most of the existing studies suffered from weaknesses related to skin identification. Therefore, to achieve this goal, we exploit the visual style elements of clothes to capture the discriminative attributes of each clothing genre and utilize an adaptive skin color model based on hill-climbing segmentation and Particle Swarm Optimization (PSO) to identify the skin color. The experimental results show that integrating these two approaches into a clothing recognition framework can lead to significant improvements over baselines, achieving new state-of-the-art results. Importantly, our method achieves these satisfactory results with a compact representation that does not require a large amount of training data to generate.
{"title":"Adaptive Skin Color Model for Clothing Genre Recognition via Particle Swarm Optimization","authors":"S. Hidayati, Erliyah Nurul Jannah, Y. Anistyasari","doi":"10.1109/ICTS52701.2021.9608235","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608235","url":null,"abstract":"Clothing genre recognition has shown its capabilities in many intelligent fashion scenarios. Given an unannotated consumer photo of a full-body person, the proposed study addresses the problem of recognizing the upperwear genres presented in that photo. Although the topic continues to show progress, most of the existing studies suffered from weaknesses related to skin identification. Therefore, to achieve this goal, we exploit the visual style elements of clothes to capture the discriminative attributes of each clothing genre and utilize an adaptive skin color model based on hill-climbing segmentation and Particle Swarm Optimization (PSO) to identify the skin color. The experimental results show that integrating these two approaches into a clothing recognition framework can lead to significant improvements over baselines, achieving new state-of-the-art results. Importantly, our method achieves these satisfactory results with a compact representation that does not require a large amount of training data to generate.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"61 1","pages":"155-160"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77425419","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608854
Anindita Septiarini, H. Hamdani, Emy Setyaningsih, Edwanda Arisandy, S. Suyanto, E. Winarno
The optic nerve head (ONH) is a sphere area with light-colored on the fundus image. It needs to be observed by an ophthalmologist to detect glaucoma. Glaucoma is an eye disease that may cause permanent blindness. It can be detected based on the cup-to-disk ratio (CDR) value. This value is generated by calculating the diameter length of the ONH. In order to perform these calculations, it is necessary to segment the ONH area. This study aims to develop an ONH area segmentation method that consists of four main processes: detection of the region of interest (ROI), pre-processing, segmentation and post-processing. ROI detection is implemented in the green channel using the OTSU method, followed by pre-processing using the median filtering, which aims to discard the blood vessel. Furthermore, K - Means is applied to the segmentation process, followed by post-processing using several morphological operations to remove the appearance noise. This method successfully achieves the F1score value of 0.941 with test data of 68 images.
{"title":"Automatic Segmentation of Optic Nerve Head by Median Filtering and Clustering Approach","authors":"Anindita Septiarini, H. Hamdani, Emy Setyaningsih, Edwanda Arisandy, S. Suyanto, E. Winarno","doi":"10.1109/ICTS52701.2021.9608854","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608854","url":null,"abstract":"The optic nerve head (ONH) is a sphere area with light-colored on the fundus image. It needs to be observed by an ophthalmologist to detect glaucoma. Glaucoma is an eye disease that may cause permanent blindness. It can be detected based on the cup-to-disk ratio (CDR) value. This value is generated by calculating the diameter length of the ONH. In order to perform these calculations, it is necessary to segment the ONH area. This study aims to develop an ONH area segmentation method that consists of four main processes: detection of the region of interest (ROI), pre-processing, segmentation and post-processing. ROI detection is implemented in the green channel using the OTSU method, followed by pre-processing using the median filtering, which aims to discard the blood vessel. Furthermore, K - Means is applied to the segmentation process, followed by post-processing using several morphological operations to remove the appearance noise. This method successfully achieves the F1score value of 0.941 with test data of 68 images.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"1 1","pages":"118-122"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85446174","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9608553
Muhamad Dian Manunggal, Y. Ruldeviyani
The organization has implemented work from home policy for its employees since the outbreak of global coronavirus disease, this policy was taken to prevent the spread and transmission of virus in the office environment. However, organization must ensure that governmental activities and public services continue and run normally by optimizing e-government. E-Government was regulated in Presidential Decree Number 95 Year 2018 concerning Electronic-Based Government Systems, the regulation rules government system by utilizing information and communication technology to provide services for stakeholders. This research analyzes critical success factors which influence and determine the success of e-government during work from home implementation. Twelve factors have been identified on three categories, namely “Organization”, “Technology” and “Human Resources”. These factors were analyzed by Analytical Hierarchy Process method to obtain critical success factors. As a result, “Human Resource” is the most important criteria, followed by “Organization” and “Technology”, then the most important factors of these criteria which influence and determine the success of e-government during work from home implementation are “Health”, “Incentive” and “Infrastructure”, respectively.
{"title":"Critical Success Factors Analysis of E-Government during Work from Home Implementation: A Case Study at Government Organization in Indonesia","authors":"Muhamad Dian Manunggal, Y. Ruldeviyani","doi":"10.1109/ICTS52701.2021.9608553","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9608553","url":null,"abstract":"The organization has implemented work from home policy for its employees since the outbreak of global coronavirus disease, this policy was taken to prevent the spread and transmission of virus in the office environment. However, organization must ensure that governmental activities and public services continue and run normally by optimizing e-government. E-Government was regulated in Presidential Decree Number 95 Year 2018 concerning Electronic-Based Government Systems, the regulation rules government system by utilizing information and communication technology to provide services for stakeholders. This research analyzes critical success factors which influence and determine the success of e-government during work from home implementation. Twelve factors have been identified on three categories, namely “Organization”, “Technology” and “Human Resources”. These factors were analyzed by Analytical Hierarchy Process method to obtain critical success factors. As a result, “Human Resource” is the most important criteria, followed by “Organization” and “Technology”, then the most important factors of these criteria which influence and determine the success of e-government during work from home implementation are “Health”, “Incentive” and “Infrastructure”, respectively.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"20 1","pages":"78-83"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89732406","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 : 2021-10-20DOI: 10.1109/ICTS52701.2021.9607897
Ardiansyah, D. Liliana
The COVID-19 pandemic has brought challenges in the field of biometrics to be able to carry out biometric identification on masked faces. Various studies on biometric identification on masked faces have been carried out and some have obtained promising results. This study aims to obtain a biometric identification method for masked faces using the JAFFE database dataset which has been manipulated into masked face images. The proposed method in this study can produce an accuracy value of 96%, which is promising enough to be applied in the biometric industry. The proposed method uses the face area segmentation technique and extraction of local binary pattern and histogram of oriented gradient features with the Support Vector Machine classification method.
{"title":"Facial Biometric Identification in The Masked Face","authors":"Ardiansyah, D. Liliana","doi":"10.1109/ICTS52701.2021.9607897","DOIUrl":"https://doi.org/10.1109/ICTS52701.2021.9607897","url":null,"abstract":"The COVID-19 pandemic has brought challenges in the field of biometrics to be able to carry out biometric identification on masked faces. Various studies on biometric identification on masked faces have been carried out and some have obtained promising results. This study aims to obtain a biometric identification method for masked faces using the JAFFE database dataset which has been manipulated into masked face images. The proposed method in this study can produce an accuracy value of 96%, which is promising enough to be applied in the biometric industry. The proposed method uses the face area segmentation technique and extraction of local binary pattern and histogram of oriented gradient features with the Support Vector Machine classification method.","PeriodicalId":6738,"journal":{"name":"2021 13th International Conference on Information & Communication Technology and System (ICTS)","volume":"3 1","pages":"129-133"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89744658","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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