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Author: search.filters.author.Tangtisanon, PikulkaewSubject: search.filters.subject.machine learning

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    A Portable Electronic Nose for Real-time Monitoring of Food Spoilage Using Multiple Machine Learning Models
    (2025-01-01)
    Tangtisanon, Pikulkaew
    ;
    Grodniyomchai, Boonyawee
    In this study, we present the design and development of a portable electronic nose (E-nose) system for detecting and classifying spoiled household food through the application of machine learning (ML) techniques. The targeted odors include fungi from bread, spoiled rice, spoiled milk, yoghurt, rotten egg, rotten boiled egg, rotten pork, and rotten beef, totaling eight odor classes. A total of 1800 samples were collected using three gas sensors and one temperature sensor. After outlier removal with Isolation Forest, 1000 samples remained. Multiple ML models were trained and evaluated over ten iterations, comparing classification accuracy and processing time. Among all the models, the k-nearest neighbor (KNN) achieved the highest performance, with an average accuracy of 99.889% and an average processing time of 0.167477 s. The decision tree (DT) model followed closely with an accuracy of 99.843% and required a significantly less processing time of 0.012080 s. Although DT has a slightly lower accuracy than KNN, its processing time is 13.86 times faster. For our scenario that requires real-time results, DT is a better choice than KNN. The proposed portable E-nose demonstrates strong potential for realworld applications such as food spoilage detection, environmental monitoring, and health diagnostics.
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    Enhancing Odor Classification of Essential Oils with Electronic Nose Data
    (2024-01-01)
    Grodniyomchai, Boonyawee
    ;
    Satcharoen, Kleddao
    ;
    Tangtisanon, Pikulkaew
    In the current business landscape, the fragrance industry has gained substantial prominence. In this context, there is a requirement to create the most compact and sufficiently accurate model possible, suitable for deployment on a portable device. The aim is to develop a model capable of effectively classifying various fragrance types based on data pertaining to air properties and fragrance component attributes. This paper presents the feature extraction from the dataset electronic node to classify odor types using a machine learning model compared before and after the feature extraction of the dataset. In our investigation, we employed datasets of varying sizes, including small datasets (composed of 1000 samples), large datasets (composed of 10000 samples), and raw datasets (composed of 21000 samples). This methodology was employed to discern disparities in model performance, average accuracy, and computational runtime across these different dataset sizes. We observed that the decision tree model, post-training with principal component analysis, showed a performance improvement when compared to the basic machine learning model. Specifically, the decision tree model achieved accuracy rates of 100.00%, 99.97%, and 97.00% respectively.
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    COVID-19 Pandemic Prevention Mobile Application for on Campus Classroom
    (2021-04-23)
    Tangtisanon, Pikulkaew
    COVID-19 pandemic is a novel coronavirus that has not been found in humans before. This virus can be transmitted to other humans primarily through respiratory secretions when an infected person coughs or talks. To avoid human to human transmission of this pandemic, the government extend the state of emergency policies which cause vital damage for many business sections worldwide including educational institution. Many schools decide to let the students learn at home. However, in practical courses such as a chemical workshop, students must come to the laboratory room to perform experiments which may increase the risk of infection. In order to prevent the spread of COVID-19 between students and staff, anybody entering the school must conduct a risk assessment, measure a body temperature and wear a face mask at all times. Many COVID-19 contact tracing platforms allow users to assess infection risk and notify if they have been exposed to infected persons. Unfortunately, they cannot be used effectively with the on-campus education system. The proposed mobile application was developed to handle the needs of the onsite education system during the ongoing COVID-19 situation in schools. The application contains three main functions which are a COVID-19 self-assessment, a roll-call, and a social distancing function. This paper focused on the roll-call function using face recognition and Global Positioning System (GPS). In a normal situation, the student just opens an application, shows his or her face to a smartphone camera then the application will detect a face part and easily recognize the student's identification. However, in the new normal situation where everyone must wear a mask, it will be a very difficult task to perform face recognition since almost half of the face is hidden. The convolutional neural network (CNN) was applied to train a CNN model using a dataset of 18 peoples with non face mask wearing and face mask wearing. The face mask wearing consisted of three different face mask types: Disposable surgical mask (DS), N95 face respirators (N95) and general 3D mask (3D). After that, the model was exported to the proposed mobile application. Experimental results on a realworld dataset show that the proposed model can be used with a high accuracy rate in non face mask samples. In face mask samples, the 3D mask has the highest accuracy rate.
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    Web Service Based Food Additive Inventory Management with Forecasting System
    (2018-09-11)
    Tangtisanon, Pikulkaew
    Recently, food industries have been growing rapidly due to the development of novel technology. Numerous research has been conducted to improve products to satisfy the needs of customers. As a result, various food additives have been used to compose the product and which makes it difficult in recognizing and managing food additive stock. To be able to survive in a competitive world, the industry must find a practical stock management solution since under-stocking causes the industry to lose an opportunity to sell while overstocking causes a deficit. This paper focuses on an inventory management and a stock forecasting system. Web service was implemented as a new approach for an inventory management system that helps to manage and to find the food additives that exist in the international food additive database authorized by Codex Alimentarius Commission. Using web services has many advantages than a traditional web base. The service provider does not have to reveal the database access method to the client, and the information or business model can be changed at any time, and no need to update the client side. The client can access the service via any platform. The web service has been developed through Hypertext Mark up Language 5 (HTML5), Node JavaScript (NodeJS), and My Structured Query Language (MySQL), Database Management System, Hypertext Preprocessor (PHP). The stock forecasting was done by Python with four machine learning models which are Naive Bayes, Decision Tree, Linear Regression and Support Vector Regression to predict stock of food additive. Accuracy is used to measure the performance of these techniques. The experimental result indicated that the most accurate model for stock forecasting is Linear regression.