Chantamit-O-Pass, Pattanapong

Loading...
Thumbnail Image
Preferred name
Chantamit-O-Pass, Pattanapong
Alternative Name
Chantamit-O-Pas, Pattanapong
Main Affiliation
Email
pattanapong.ch@kmitl.ac.th

Settings

Now showing 1 - 6 of 6
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Thai Recipe Retrieval Application Using Convolutional Neural Network
    (2022-01-01)
    Phophan, Thitiwut
    ;
    Khuthanon, Rungwaraporn
    ;
    Chantamit-o-Pas, Pattanapong
    Due to the COVID-19, self-catering captured the interest of many people. This paper proposes a novel mobile application, which can share recipes and recognition material to help individuals with low prior cooking skill. It offers good, practical knowledge and can help to build cooperative teams in the cooking community among novice cooks. Choosing the ingredients for cooking can be difficult. This is especially true because of Thai vegetables look similar such as white and sweet basil particularly for new cooks not familiar with their other characteristics. This research introduces a mobile application, Kin Rai Dee App, which is based on sharing recipes and recognition material by using Roboflow with a pretrained model. To develop Thai vegetable image classification in our mobile application, the Convolutional Neural Network technique and a Thai vegetable dataset is used to evaluate the performance of our classification model. This dataset is composed of two sources including (1) Thai herb dataset from Kaggle website and (2) our own images. Therefore, there are totally 12 classes in the Thai vegetable dataset with image’s resolutions of 224 × 224 pixels. The result for image training is implemented through machine learning and Roboflow methods. The experiments process has training results accuracy at 85% and testing result at 15% in both models. The performance of our model has proven that it can achieve the result with confidence values 100% and 99.21% for specific Thai vegetables.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Deep Learning Based Automobile Identification Application
    (2023-01-01)
    Chantamit-o-Pas, Pattanapong
    ;
    Sangaroon, Pattanayu
    ;
    Srisura, Jukkapat
    Today, the high competition among domestic automobile manufacturers is intense situation than previous years. This result gives advantages in a good variety of brands, models, engine sizes and appearances. This can cause some critical issues in recognizing and recalling a car by manufacturer. In addition, an owner may modify some parts of original vehicle such as the head bumper, the rear bumper, and the head light. This modification also affects the people who are looking for pre-owned cars. Despite the fact, the details are mismatch with the vehicle registration book that issued by the Department of Land Transport. From this incident, the researchers implemented a convolutional neural network (CNN) in the identification of vehicle characteristics to reduce the ambiguity for each car’s models. The researchers conducted experiments using five algorithms. SVM, ResNet34, ResNet50 and Inception-ResNetV2. The researchers set up a library of two car models, Toyota Hilux and Honda Civic sedan and Civic Hatchback, including models from past ten years ago until the present. The images are of 224 × 224 pixels. The data are categorized into two sets, a training set has 1,449 images which is counted as 80% of total images and a testing set is having 362 images which is about 20% of total. The total images are 1,811 and 26 Classes. Our experiments compared the accuracies of SVM, ResNet34, ResNet50, and Inception-ResNetV2, which came out to be 21.4%, 55.5%, 66.6%, and 92.8% respectively. As a result, Inception-ResNetV2 outperforms among all other methods.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Long Short-Term Memory Recurrent Neural Network for Stroke Prediction
    (2018-01-01)
    Chantamit-o-Pas, Pattanapong
    ;
    Goyal, Madhu
    Electronic Healthcare Records (EHRs) describe the details about a patient’s physical and mental health, diagnosis, lab results, treatments or patient care plan and so forth. Currently, the International Classification of Diseases, 10<sup>th</sup> Revision or ICD-10 code is used for representing each patient record. The huge amount of information in these records provides insights about the diagnosis and prediction of various diseases. Various data mining techniques are used for the analysis of data deriving from these patient records. Recurrent Neural Network (RNN) is a powerful and widely used technique in machine learning and bioinformatics. This research aims at the investigation of RNN with Long Short-Term Memory (LSTM) hidden units. The empirical research is intended to evaluate the ability of LSTMs to recognize patterns in multi-label classification of cerebrovascular symptoms or stroke. First, we integrated ICD-10 code into health record, as well as other potential risk factors within EHRs into the pattern and model for prediction. Next, we modelled the effectiveness of LSTMs for prediction of stroke based on healthcare records. The results show several strong baselines that include accuracy, recall, and F1 measure score.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    The Effects of an Individual and Family Self-Management Program for Slowing Disease Progression via a Mobile Application on Self-Management Behaviors and Clinical Outcomes in Patients with Stage 3 Chronic Kidney Disease in Thailand: A Quasi-Experimental Study
    (2026-01-01)
    Kulsuwan, Suphitsara
    ;
    Chantamit-O-Pas, Chutima
    ;
    Ponpinij, Panicha
    ;
    Chantamit-O-Pas, Pattanapong
    Purpose: Stage 3 chronic kidney disease (CKD) is highly prevalent and often progresses to end-stage renal disease without effective self-management. This study examined the effects of an Individual and Family Self-Management Program delivered via a mobile communication application on self-management behaviors and clinical outcomes among patients with stage 3 CKD. Methods: A 12-week quasi-experimental study with a pretest-posttest control group design was conducted among 50 patients with stage 3 CKD at a tertiary hospital in Thailand, between January and May 2024. The intervention group received a self-management program grounded in Individual and Family Self-Management Theory and delivered via the LINE chatbot. The program included educational content, self-regulation strategies, and structured family support, while the control group received usual care. Outcomes included self-management behaviors, estimated glomerular filtration rate (eGFR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and hemoglobin A1c (HbA1c). Analysis of covariance was used to adjust for baseline differences. Results: After adjustment for baseline values, the intervention group showed significantly greater improvements in self-management behaviors (F=7.92, p<.05) and eGFR (F=52.92, p<.001) compared with the control group. Significant reductions were also observed in SBP (F=26.84, p<.001), DBP (F=12.61, p<.05), and HbA1c levels (F=7.74, p<.05). Conclusion: A mobile-based Individual and Family Self-Management Program effectively improved self-management behaviors and key clinical outcomes among patients with stage 3 CKD, supporting the integration of family engagement and digital technology in chronic disease care.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Knowledge Acquisition, Representation and Transfer in the Metamodel for Software Development Methodologies
    (2019-10-01)
    Chantamit-O-Pas, Pattanapong
    At present, software development life cycle has many standards involved. However, during the process of creating components in an application, there are many problems that originate in the knowledge gap between stakeholders and developers. The problem of developing is issues such as ambiguity, misunderstanding, miscommunications, different domain skills, and conflicting goals. Metamodel are needed because the methodology contents must be purpose-fit and optimally adjusted for the users/stakeholders/organisation. Metamodel, a model of software model includes endeavor domain, method domain, and metamodel domain. It has contents for user's needs, including organization, project, and product aspect. Three interconnected meta-elements are work products, work units, and producers, as well as relationship between them. ISO/IEC 24744 is the standard that includes metamodels.Application software are now applied knowledge management components aiming to reduce the gap between users/stakeholders and software developer team. The knowledge can be shared and transferred to others. This paper, therefore, focuses on applying knowledge management metamodel for software development process. We will extend the metamodel concept in ISO/IEC 24744 for knowledge acquisition, knowledge representation, and knowledge transfer to WorkUnits and WorkProducts, thereby increasing their explicitness in the software development process. Tacit knowledge will be focused and propose knowledge metamodel will be shown.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Road Traffic Injury Prevention Using DBSCAN Algorithm
    (2020-01-01)
    Chantamit-o-pas, Pattanapong
    ;
    Pongpum, Weerakorn
    ;
    Kongsaksri, Krisakorn
    Machine learning has been used in innovation research for the last two decades. It is widely applied in decision making such as clustering, analysis, predicting, evaluating prognosis, and recommendation. The car accident often causes death or disability in most countries. Road accident victims usually have poor quality of life because of serious illness, long-term disability, which is a huge burden to their families and some eventually died. The behavior of driving on road is a major risk factor to road traffic. This research develops a mobile application that can notify the driver when there is a risk nearby. It focuses on Thailand and it is applied only to local cases. The dataset comes from Thai Road Safety Collaboration (ThaiRSC), which is a non-governmental organization that records a lot of daily accident cases. It uses the DBSCAN algorithm, a clustering technique, for road traffic injury prevention applied on the ThaiRSC’s dataset that focused on 3 districts of Bangkok, namely Ladkrbang, Pravet, Suan Lung, as well as all province in eastern Thailand. The outcomes of this research are beneficial in warning drivers if they are likely to encounter a road accident.