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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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Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification
(2024-01-01)
Purahong, Boonchana
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Krungseanmuang, Woranidtha
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Tenghongsakul, Kasi
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Archevapanich, Tuanjai
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Khunthawiwone, Parkpoom
This paper presents a novel method for detecting defects in printed circuit boards (PCBs) using an ensemble of classifiers based on the Choquet fuzzy integral. Our approach employs convolutional neural network (CNN) models, specifically ResNet152, VGG19, and InceptionV3 as base classifiers to identify six types of PCB defects: spurs, mouse bites, short circuits, open circuits, spurious copper, and pinholes. Given the critical role of PCBs in ensuring electronic equipment reliability, effective defect detection methods like ours are essential. We employ pre-trained CNN models for feature extraction and classification of PCB defects. Following this, we combine the prediction scores using the Choquet fuzzy integral to derive more accurate final labels, exceeding the accuracy of standalone models. Our approach is tested on PCB images obtained from public repositories, captured using a linear scan CCD. The evaluation results demonstrate average precision, recall, F-score, and accuracy of 93.0%, 95.2%, 95.1%, and 95.1%, respectively.
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CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images
(2023-02-01)
Kanjanasurat, Isoon
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Tenghongsakul, Kasi
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Purahong, Boonchana
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Lasakul, Attasit
The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both chest X-ray and computed tomography (CT) images are capable of accurately diagnosing COVID-19. To distinguish lung illnesses (i.e., COVID-19 and pneumonia) from normal cases using chest X-ray and CT images, we combined convolutional neural network (CNN) and recurrent neural network (RNN) models by replacing the fully connected layers of CNN with a version of RNN. In this framework, the attributes of CNNs were utilized to extract features and those of RNNs to calculate dependencies and classification base on extracted features. CNN models VGG19, ResNet152V2, and DenseNet121 were combined with long short-term memory (LSTM) and gated recurrent unit (GRU) RNN models, which are convenient to develop because these networks are all available as features on many platforms. The proposed method is evaluated using a large dataset totaling 16,210 X-ray and CT images (5252 COVID-19 images, 6154 pneumonia images, and 4804 normal images) were taken from several databases, which had various image sizes, brightness levels, and viewing angles. Their image quality was enhanced via normalization, gamma correction, and contrast-limited adaptive histogram equalization. The ResNet152V2 with GRU model achieved the best architecture with an accuracy of 93.37%, an F1 score of 93.54%, a precision of 93.73%, and a recall of 93.47%. From the experimental results, the proposed method is highly effective in distinguishing lung diseases. Furthermore, both CT and X-ray images can be used as input for classification, allowing for the rapid and easy detection of COVID-19.
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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
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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.
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Inventory Management System based on IoT and Microservices Architecture Design
(2023-01-01)
Sithiyopasakul, Paisan
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Piyatananugoon, Chavinkorn
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Chaowalittawin, Vasutorn
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Krungseanmuang, Woranidtha
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Sathaporn, Posathip
This paper presents the inventory management system based on IoT and microservices architecture that synced between IoT (Internet of Things) and web application. There are two main parts consisting of IoT forklift vehicle and data transmitted system by MQTT protocol. Microservice can utilize data to process business logic and provides API. Inventory area includes a zone, a subzone and parking area for forklifts to scan QR code on each subzone. Data of forklifts is published to back-end service. The results of system microservice publish information events and processes business data to the admin client with a delay under a second.
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Deep transfer learning for brain tumor detection based on MRI images
(2023-01-01)
Tenghongsakul, Kasi
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Kanjanasurat, Isoon
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Archevapanich, Tuanjai
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Purahong, Boonchana
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Lasakul, Attasit
Brain tumors are among the main causes of cancer-related mortality in humans. Early detection of brain tumors is a vital job in the medical task of diagnosis and cure planning for patients. The automatic detection greatly facilitates medical personnel. Magnetic resonance imaging (MRI) is an accepted imaging strategy for diagnosing brain tumors. Presently, deep learning approaches have proven effective in handling various computer vision problems, such as image classification, because of their high performance and also determine models that can learn and decide based on sample data. In this study, the deep transfer learning method, namely InceptionResNet-V2, ResNet50, MobileNet-V2, and VGG16, was used to compare and find the most suitable model for brain tumor detection from the public MRI dataset. Also, CLAHE was employed as an image enhancement technique to improve the quality of the image data set before being used as the model input. As a result, the suggested method performed a prediction accuracy of up to 100%.
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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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Retinal Blood Vessel Extraction by Using Pre-processing and IterNet Model
(2020-12-03)
Tenghongsakul, Kasi
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Kanjanasurat, Isoon
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Purahong, Boonchana
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Lasakul, Attasit
At present, many of visual disease happened from the abnormality of retinal vessels. The automatic vascular extraction from fundus images is essential for the diagnosis to reduce vision loss. This paper offers retinal blood vessel segmentation using the pre-processing and IterNet model, a convolution neural network. The green channel and gray scale image that is high contrast between the blood vessel and background, including the normalization, were used to improve blood vessel image quality. The proposed method was tested with two widely used databases, including DRIVE and CHASEDB-1, which unique characteristics in each data set. The results of blood vessel extraction of Drive and CHASEDB-1 achieved sensitivity 0.8126 and 0.7541, respectively.
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A robust image watermarking using multiresolution analysis of wavelet
(2005-12-01)
Temi, Chirawat
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Choomchuay, Somsak
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Lasakul, Attasit
Embedding watermark in the wavelet becomes more attractive to most researchers as it could provide better performance. In this paper present a method of embedding binary visualized image into the host image by modifying coefficients of wavelet domain in LL bands with appropriate strength factor in order to compromise between acceptable imperceptibility level and attacks' resistance. Qualified Significant Wavelet Tree (QSWT) is used in both to select locations where watermark data are to be embedded, and to find locations of watermark in the extraction process. Results show that the proposed method successfully achieves robustness level of various attacks such as image processing, rotation attack etc. © 2005 IEEE.
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Adaptive watermarking in spatial domain for still image
(2004-12-01)
Kimpan, Somchok
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Lasakul, Attasit
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Kimpan, Chom
In this paper, watermarking for still image is proposed Image watermarking is performed in spatial domain that not only easy but also good result. A watermark image as binary image is embedded onto a original image by using method that gray levels of pixels in original image blocks is modified to appropriate an intensity of block. A variation of watermark image bits in order to embed the original image block selected affects to embedded block intensity and also it depends on original image block intensity. The block size is adapted as intensity of original image and capacity of watermark image in order to embed. As method of varying block size proposed, the effect of block size adaptation is good and also watermark image is robust to a number types of degradation. As the proposed method, qualify of the original image is at least affected.

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