Purahong, Boonchana

Preferred name

Purahong, Boonchana

Alternative Name

Purahong, B.

Main Affiliation

School of Engineering

boonchana.pu@kmitl.ac.th

Now showing 1 - 10 of 11

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Blood Vessel Extraction and Optic Disk Localization for Diabetic Retinopathy
(2020-09-15)
Kanjanasurat, Isoon
;
Purahong, Boonchana
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Pintavirooj, Chuchart
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Satayarak, Nitjaree
;
Benjangkaprasert, Chawalit
This paper presents methods of vascular extraction and optic disk localization in the retinal images. Our approach begins with preprocessing to improve the quality of blood vessels. In the next step, a matrix filter was applied to express blood vessels. Finally, the blood vessel structure was used to estimate the location of the optic disk. The proposed method was tested on all different forty retinal images from the DRIVE database, which public retinal image dataset. The results of vessel extraction were compared with the ground truth image. The error of vascular extraction showed that the average sensitivity and accuracy were 79.81% and 94.98%, respectively. The optic disk localization achieved 97.5%.
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Automated resource management system based on kubernetes technology
(2021-05-19)
Sithiyopasakul, Jirayus
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Archevapanich, Tuanjai
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Purahong, Boonchana
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Sithiyopasakul, Paisan
;
Benjangkaprasert, Chawalit
The purpose of this research is intended to study and analyze the Kubernetes technology using the processes of performance evaluation by inspecting how many requests and responses could the server handle. According to the ability of Kubernetes technology, there is no systematic measure of performance despite the Kubernetes is currently in use broadly. Therefore, this paper proposed to test out the system by measuring the effectiveness according to a structured process by studying such measurement variables including the number of requests per second, number of responses to requests, and resource extension period with Kubernetes technology. Which from the testing and analysis of all three variables as mentioned, it is possible to know the efficiency of the Kubernetes technology in such a similar environment. Moreover, the testing experiment could display a piece of information on the dashboard for visualization and analytic purposes.
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DDoS Detection Using a Hybrid CNN–RNN Model Enhanced with Multi-Head Attention for Cloud Infrastructure
(2025-11-01)
Sathaporn, Posathip
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Krungseanmuang, Woranidtha
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Chaowalittawin, Vasutorn
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Benjangkaprasert, Chawalit
;
Purahong, Boonchana
Cloud infrastructure supports modern services across different sectors, such as business, education, lifestyle, government and so on. With the high demand for cloud computing, the security of network communication is also an important consideration. Distributed denial-of-service (DDoS) attacks pose a significant threat. Therefore, detection and mitigation are critically important for reliable operation of cloud-based systems. Intrusion detection systems (IDS) play a vital role in detecting and preventing attacks to avoid damage to reliability. This article presents DDoS detection using a convolutional neural network (CNN) and recurrent neural network (RNN) model enhancement with a multi-head attention mechanism for cloud infrastructure protection enhances the contextual relevance and accuracy of the DDoS detection. Preprocessing techniques were applied to optimize model performance, such as information gained to identify important features, normalization, and synthetic minority oversampling technique (SMOTE) to address class imbalance issues. The results were evaluated using confusion metrics. Based on the performance indicators, our proposed method achieves an accuracy of 97.78%, precision of 98.66%, recall of 94.53%, and F1-score of 96.49%. The hybrid model with multi-head attention achieved the best results among the other deep learning models. The model parameter size was moderately lightweight at 413,057 parameters with an inference time in a cloud environment of less than 6 milliseconds, making it suitable for application to cloud infrastructure.
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Comparison of Support Vector Machine for Apron Allocation
(2022-05-27)
Kanjanasurat1, Isoon
;
Purahong, Boonchana
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Teerapanpong, Saowaluk
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Benjangkaprasert, Chawalit
This paper presents machine learning techniques for classifying parking stand locations in the apron allocation management service that affects total airport ground service processing time at airports where arriving aircraft land. SVM and Kernel SVM algorithms will be used, as well as Polynomial, Gaussian RBF, and Sigmoid, based on five input factors: aircraft identification, estimated time of arrival (ETOA), area of apron, type of aircraft, and target of stands. Then, we compared classification accuracy and performance using the Mean Absolute Error (MAE) and the squared mean error (Root Mean Square Error: RMSE), and discovered that the Gaussian RBF kernel of the SVM algorithm model is more accurate than the other model. This work may be beneficial in assisting airport's decision-makers and enhancing airport operations efficiency and predictability.
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Landing Runway Assignment by Airport Traffic using Machine Learning
(2022-05-27)
Kanjanasurat1, Isoon
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Jungsuwadee, Wasarut
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Purahong, Boonchana
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Benjangkaprasert, Chawalit
This paper presents the solutions to the overwhelming burden of air traffic controllers by reducing workload and optimizing runway capacity using machine learning tools to assign runways for incoming aircraft based on critical information such as aerodrome traffic information of aircraft taking off and landing on the runway at Suvarnabhumi Airport, THAILAND. The model is composed of four layers and three hidden layers. ReLU and Adam are the activation and optimization functions used in this model, respectively. The model was trained using assigned landing runway and traffic runway factors. Predicting the assigned runway is 82.77 percent accurate.
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Implementation of Cloud Computing and Internet of Things (IoT) by Performance Evaluation
(2024-01-01)
Sithiyopasakul, Jiran
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Archevapanich, Tuanjai
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Sithiyopasakul, Saran
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Lasakul, Attasit
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Purahong, Boonchana
The integration of cloud computing and the Internet of Things (IoT) holds transformative potential across diverse industries. Performance assessment is essential to gauge the quality and efficiency of cloud computing and IoT systems. This paper presents a comprehensive performance evaluation of cloud computing and IoT systems, focusing on three major platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Experimental results encompass various scenarios, including normal operation, heavy load conditions, IoT applications, and scalability testing. The outcomes reveal distinct performance metrics such as response time, throughput, latency, and reliability for each cloud platform.
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Centralizing Data Warehouse Platform for Mining Management Using AWS Computing Instance
(2024-01-01)
Sathaporn, Posathip
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Chaowalittawin, Vasutorn
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Krungseanmuang, Woranidtha
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Benjangkaprasert, Chawalit
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Purahong, Boonchana
Mining industry is one of significant industry in the world. In order to increase efficiency, safety, sustainability and environmental impacts in mining process. This article presents a centralized data platform for mining process by design based on microservices architecture which can be supported various of input data source such as manual form, excel file and internet of things (IoT) device and notification when the value is abnormal. Our platform implementation on Amazon Web Service (AWS) cloud. For development of productivity in process and controlling quality the mine environment. The results of the platform operations with various source of data acquisition with response time is less than 1.5 seconds per each request.
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Personal identification using a delaunay triangle and optic disc retinal vascular pattern
(2020-01-01)
Kanjanasurat, Isoon
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Purahong, Boonchana
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Aoyama, Hisayuki
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Benjangkaprasert, Chawalit
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Pintavirooj, Chuchart
Retinal vascular patterns are unique and individual. They provide highly secure and correct identity authentication. In this study, we exploit an image alignment approach based on a geometric invariant, which is the area spanned by feature-point triplets for personal identification. First, we located the optic disc by using a projection of the vascular structure in vascular extraction and extracted feature points that are bifurca- tions of a retinal blood vessel in the vicinity of the optic disc as the landmarks. Delaunay triangulation is then applied to the extracted feature points. The absolute invariant is then derived by taking the ratio of successive triangular area patches. The alignment is achieved by establishing correspondences between feature points after a conformal sort- ing step based on a derived set of absolute affine invariants. The affine transformation parameters can then be calculated by the corresponding vertices of the most robust neigh- bouring triangle of both inquiry and reference images. The optic disc localization results successfully located 95.95% in six widely used retinal image databases. The algorithm of vascular extraction, applied on the DRIVE database, provided an average accuracy of ap- proximately 94.1%. The best accuracy and sensitivity for neighbouring triangle matching obtained were 99.90% and 87.66%, respectively.
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Voice over IP Integration Platform Performance Using EC2 AWS Cloud Service
(2022-01-01)
Sathaporn, Posathip
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Krungseanmuang, Woranidtha
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Chaowalittawin, Vasutorn
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Anuwongpinit, Thanavit
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Purahong, Boonchana
This article describes a method for integrating a mobile application for controlling and transmitting voice data levels in various departments within an organization with Amazon Elastic Compute Cloud (AWS EC2) to reduce hardware location costs and create more convenient in-house management at a single point. To begin, the paper introduces the project objective with a business scenario from an organization. Second, the SIP server implementation method is provided by Asterisk on AWS EC2 Ubuntu operating system and connection with a Mobile application that is used by flutter framework. Finally, the project experiments and discussions will be presented, and the obtained results show that the call setup time for the iOS/Android platforms to PC performed the best, taking less than one second, and was the fastest when compared to other testing metrics. However, there are many more metrics that should be considered, which are presented in the research's results section. With high performance and stability, this article was able to broadcast voice data via mobile applications.
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Performance Evaluation of Infrastructure as a Service across Cloud Service Providers
(2023-01-01)
Sithiyopasakul, Saran
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Archevapanich, Tuanjai
;
Purahong, Boonchana
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Sithiyopasakul, Paisan
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Lasakul, Attasit
The purpose of this research aims to monitor, analyze, and compare the performance of infrastructure as a service (IaaS) between the selective cloud providers. To assure which cloud provider has more stability, reliability, and scalability. This paper focuses on performance testing based on a deployed web server in the cloud environment. The main feature of cloud computing is scalability thus most common IaaS cloud service providers (CSPs) have Auto Scaling features for instances or virtual machines. Not only does this paper gives the experimental results of the scaling scalability testing, but it also provides the results of recovery testing to inspect how long a web server is able to recover from failures and load testing which simulated traffic requests. Testing was conducted in the major public clouds of Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). Azure performs the most efficiently of almost all testing but hardest to configure.

Purahong, Boonchana

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