This research aims to developed a Content-based image retrieval model for resolve semantic gaps problem where low-level features cannot correctly convey the meaning of images. The result of developed model consists of 3 modules: 1) build the image description set module, it applies a CLIP (Contrastive Language-Image Pre-training) to learn the meaning of images by self-supervised learning from the relationship between images and caption on image encoder and text encoder with cosine similarity before collecting to the image description set and create to an image feature vector. 2) query processing module to learn the meaning of the text and constructs it as a query feature vector, and 3) vectors matching module with similar values between image feature vectors and query feature vectors before sorting by relevance and display the result to the user. The result of image retrieval on the Flickr30k dataset with order-unaware metric had a mean of recall is 0.93 when the result was in the top 10 is very high, but anyway it also found that the main barrier to the accuracy of the results was image variation. When comparing the image retrieval results with the image custom dataset, it was found that the average of recall was in the same direction. And there is no problem that the model's performance is compromised when working with previously unseen data. Demonstrate that the model can retrieve content-based images effectively. It also supports users with search terms in the form of natural language that are based on the meaning of the image rather than the grammar of the language. This impact of results is a guideline for information retrieval in the future.
The research aimed (1) to develop and evaluate an emotion recognition model for Thai speech using artificial neural networks, (2) to enable accurate classification of human emotions, and (3) to bridge communication gaps between computers and users. A dataset from AIResearch.in.th consisting of 27,854 Thai-language sentences categorized into angry, sad, happy, frustrated, and neutral emotions. The Mel Frequency Cepstral Coefficients (MFCC) employed for the speech feature extraction. Data was pre-processed by augmentation techniques, including time stretching, pitch shifting, and noise injection. The pre-processed data trained for artificial neural network models, including a 1-dimensional Convolutional Neural Network (1D CNN), Long Short-Term Memory (LSTM), and a hybrid model (1D CNN and LSTM). Results showed that the hybrid model (1D CNN & LSTM) achieved the highest accuracy of 80.36%, followed by the 1D CNN model (77.52%) and the LSTM model (67.86%).
Community-based tourism focuses on enhancing the value of cultural and natural resources of the community. Tourists get an authentic experience from the local lifestyle and wisdom within the community. Therefore, having an application that recommends tourist spots personalized to individual tourists' needs, and a community tourism management system is essential. This research presents the development of an application platform for an adaptive personalized tourist attraction recommendation system (APTARS). The objective is to create an adaptive personalized tourist attraction recommendation model using a collaborative filtering method and developing cross-platform applications for tourists and entrepreneurs in community-based tourism management on Android and iOS operating systems, developed with React, Node.js, and Flutter, managing databases with MongoDB. The researcher evaluated the performance of the adaptive personalized tourist attraction recommendation model using the Mean Absolute Error value (MAE) and Root Mean Square Error (RMSE). The research findings indicate that the Euclidean distance is the most effective algorithm to measure user similarity for optimal recommendation performance. The appropriate number of neighboring nodes for travel destination recommendation is 25 nodes. The performance evaluation of the application platform by experts is rated as good. User satisfaction assessment of the application platform from a sample group of 40 people, comprising 10 community entrepreneurs and 30 tourists, found that the overall user satisfaction is rated very good (gif.latex?x\bar{}=4.52, S.D.=0.51). The accuracy and functional performance satisfaction had the highest mean (gif.latex?x\bar{}=4.56, S.D.=0.52), followed by satisfaction with the ability to work according to user needs (gif.latex?x\bar{}=4.53, S.D.=0.49), satisfaction with system usage (gif.latex?x\bar{}=4.50, S.D.=0.52), and satisfaction with system and information security (gif.latex?x\bar{}=4.48, S.D.=0.51) respectively.
The purpose of this research is to study the performance of 3D rendering software between Lumion and V-Ray plugin. It is a practical research that collects data from both brands of software to compare their performance. By comparing the performance in 3 issue and got the following results: Issue 1 Performance in 3D rendering speed it was found that Lumion program took less time to render than V-Ray plugin 14 minutes 14 seconds, representing 66.69 times. Issue 2 the complexity of use found that V-Ray plugin has less than 6 steps and Issue 3 The quality of 3D images after rendering found that in terms of noise, it was found that 3D images from V-Ray plugin noise is noticeable at 400% magnification, which is preceded by Lumion. In terms of reflection on the surface of the material with a smooth surface in noticeable at 100%, it was found that the 3D image of plugin V-Ray had obvious reflections, while the 3D image of the Lumion program did not reflect on the glass surface.
The purpose of this research were 1) to compare the critical thinking skills of students before and after engagement with an education digital game based on the phenomenon and 2) to study the fourth-grade student’s satisfactions towards education digital game. The study was conducted with a sample of fourth-grade students from two classrooms, each with 30 students, categorized by academic performances high, medium, and low. The research instruments included 1) a lesson plan for critical thinking skills based on the phenomenon, 2) the education digital game, 3) a critical thinking skills assessment, and 4) a satisfaction questionnaire. The data were analyzed by calculating means (M), standard deviations (SD), and t-test. Results indicated that 1) the average critical thinking skill scores of students who learned through education digital game were significantly higher than those who did not learn through education digital game at a significant level of .05, 2) the average critical thinking skill scores of students post-test through digital educational games were significantly higher than pre-test at a significant level of .05, and 3) The students satisfaction toward education digital game was at high level
The objectives of this research are as follows: 1) to develop an educational digital game implemented water cycle content belonging to science curriculum for Thai elementary school students, 2) to determine the effectiveness of the water cycle educational digital game, 3) to compare pre-test and post-test learning achievement with this water cycle educational digital game, and 4) to study the satisfaction of students towards the water cycle educational digital game. and 5) to compare pre-test and post-test learning achievement with this water cycle educational digital game. The sample group used in this research is 45 students of the fifth grade (the 2nd semester of 2022) from Banjombung school, Ratchaburi province, Thailand. The process in developing our water cycle educational digital game comprises the steps as follows: 1) game analysis, 2) game design, 3) game development, and 4) game testing, based on the rapid application development (RAD) approach to obtain quality game within the time limit. Our developed game has been evaluated by five experts in science education and technology for the determination of effectiveness of our educational digital game based on white-box and black-box testing techniques. The obtained water cycle educational digital game is composed of eight parts, which are: 1) game starting, 2) gameplay suggestion, 3) player naming, 4) game maps, 5) water cycle contents, 6) playing, 7) testing of the water cycle content learning, and 8) score conclusion. The developed game consists of five stages related to the water cycle content, which are: 1) terrestrial evaporation, 2) oceanic evaporation, 3) condensation, 4) precipitation, and 5) steam flow. The evaluation results from five experts indicate that the overall effectiveness of our game is in the highest level. The students’ satisfaction towards the water cycle educational digital game is in the high level.
The purposes of this study were 1) to study The Development of Application for Recommending Attractions with Bird’s Eye View via Augmented Reality Technology: A Case Study Khao Rang Viewpoint, Phuket Province. 2) finding the efficiency of using the application for attractions, from a bird's eye positioning through Augmented Reality, and 3) to study the satisfaction of users experience using the application for recommending attractions from a bird's eye view of Khao Rang View Point, Phuket Province. This research article examined the development of a tourist attraction recommendation application by using a bird's eye view with augmented reality technology, which used the principle of Feature Extraction image recognition by Real-Time processing using the acquired Parallax images. To development application augmented reality technology for Recommending attractions from a bird's eye a case study Khao Rang View Point, Phuket Province. The performance of the application is highest level with a mean of 4.60 and the standard deviation was 0.40 can be applied to the sample group. The satisfaction of applying the application is at its highest level with a mean of 4.75 and the standard deviation was 0.44. The application is compatible with users. Finally, this research utilization is to the benefit of tourists using the application, and this system would be effective in helping promote local tourism.
The objectives of this research were to design and develop a personalized food recommendation system for psychiatric patients. The proposed system could be used as a tool for staff in hospitals with no nutritionists. The research methodology was based on System Development Life Cycle (SDLC) concept. First, collect patient data in the provinces of Nakhon Ratchasima, Chaiyaphum, Buriram, and Surin and determine the relationship between personal factors, e.g., gender, age, weight, height, waistline, blood type, and life elements. These factors have been taken into account with three mental disorders, namely schizophrenia, depressive, and mental and behavior disorders due to use of alcohol. Patients suffering from these three disorders must be given psychiatric medications that affect their appetite and nutritional needs differently. Finally, the IF-THEN rules were established and applied as conditions for the personalized food recommendation. The proposed system was developed as an easy-to-use web application. The results of the usability assessment indicated that the proposed system was appropriate for recommending personalized foods to psychiatric patients. Furthermore, the user provided the overall satisfaction results of the proposed system at a very good level. This system could be applied to other psychiatric hospitals or network hospitals in the community that are responsible for following up and treating psychiatric patients, but there were neither doctors nor nutritionists to recommend.
The aims of this research are to compare the efficiency of imbalance techniques between over sampling and hybrid methods and to compare performance of classification techniques: random forest, logistic regression, and support vector machine, via simulation. The study is given by high imbalanced data and predicted variables which are mostly categorical data. The criteria of the simulation are sample sizes, ratio of the number of predicted variables between categorical variables and continuous variables, and odds ratio. The results shown that balancing data with over sampling method before classify had higher accuracy, sensitivity, and specificity than hybrid method in each sample sizes. In addition, the balanced data classified with random forest had the highest accuracy, sensitivity, and specificity, the average were 0.996, 0.999 and 0.998 respectively. Moreover, logistic regression technique yields less accurate classification when the number of categorical variables is higher. The result of research can be used as a guideline for choosing a data balancing method which appropriate to data conditions in real situations.
The purpose of this research is to study the features that affect the results of the test for Digital Literacy by using the Feature Selection technique to compare the efficiency of Data Classification. The research process follows the steps of CRISP-DM, studying and collecting 12,374 records of 67 features of the Office of the National Digital Economy and Society Commission's survey and assessment of the state of digital literacy. Carry out Data Cleaning and Data Transformation into an appropriate format. SMOTE (Synthetic Minority Oversampling Technique) was used to improve the data balance. Features were selected using weighting calculation techniques: 1) Chi-Square Statistic 2) Gini Index 3) Gain Ratio 4) Information Gain and 5) Correlation-based. Using the results of the first 10 calculated highest weights of each technique to calculate the frequency and Features with frequencies higher than 2 were selected to design and create forecasting models of 5 techniques: Decision Tree, Gradient Boosted Trees, Random Forest, Naïve Bayes, and Deep Learning. Evaluation of the forecasting model using the K-Fold Cross Validation Test method, dividing the data into 10 folds, 20 folds, and 30 folds, measuring precision, recall, specificity, accuracy, F1-measure, and G-means. The results of the Study Feature Affecting the Digital Literacy Test revealed that there were 6 features with the highest frequency, frequency 5, as follows: the use of advertising media/product labels, using digital media for social media, using digital media to access websites, the problem of expensive internet service fees, problems accessing internet service areas and problems with spam/advertising messages. The comparative results of data classification efficiency test results of digital literacy found that the random forest technique was the most effective in data classification. When dividing the dataset into 30 parts, the accuracy was 76.29%, the overall efficiency was 76.00%, and the geometric mean was 76.28%.
This study aimed to design and develop the automated Coconut Dehusking Machine using Raspberry Pi as minicomputer due to its flexibility and credibility of powerful CPU that send notification via GSM to mobile phone that is essential for counting dehusked coconut without using other computer peripheral devices. In addition, the device is equipped with limit switch to count the coconuts that have been dehusked and 16 x 2 sized LCD for displaying number of dehusked coconut. Furthermore, the device is operated by 2 horse-powered Electric Motor in rotational speed of 1,800 rpm under 220 volts main power source. A gearbox, roller chain and sprocket with 60 x 3 in sizes are used extremely to decrease the speed and at the same time may increase torque of the motor. This study was conducted at SLSU Lucena determined to be of help not for individual farmers but for mass use thru cooperativism or other banking financial schemes which adhere to modern way of farming. The researchers followed the process of developmental research through planning, designing, testing, modifying and evaluating the acceptability of the machine by the technical experts as respondents chosen purposively. From the gathered data, analyzed and interpreted, the developed machine is highly acceptable. After the modification it has been proven that the existing coconut de-husking machine is more efficient in terms of saved time and energy, more productive, easy to operate, human and environmentally friendly. Moreover, the machine performed above 92% efficiency in all the tests cases which significantly revealed the efficiency performance of eighty-seven percent (87%) over-all efficiency performance out of one hundred fourteen (114) coconuts in ten series of actual testing.
The four purposes of this study were: First, to develop e-Learning with cuteness in science teaching about plants and animals for grade 4 students with hearing hearing-impaired who have different learning styles. Second, to study learning styles via e-Learning with hearing-impaired students who have different learning styles. Third, to examine the learning achievement before and after this e-Learning with cuteness, and finally, to study students' learning development who had different learning and studied via e-Learning with cuteness. This study is experimental research using one group pre-test and post-test design. The research instruments used for this experiment were: 1) e-Learning with cuteness in science teaching for grade 4 under the topic “plants and animals," pre-test and post-test exams, and Kolb’s learning style questionnaire. The samples for this study were 15 grade 4 students with hearing impairments at Surin School for the Deaf under the office of special education administration. Statistics employed for data analysis were mean ( ), standard deviation (S.D.), t-test, and f-test. The findings revealed that: The efficiency of e-Learning with cuteness in science teaching for grade 4 hearing-impaired students with different learning styles was highly efficient, meeting the requirement of Meguigans' criteria. The students’ learning achievement after learning via this e-Learning was significantly higher at the 0.05 level, while there is no significantly different in the learning achievement of students with different learning at the 0.05 level. However, learning via this e-Learning students’ achievement yielded a mean score of the learning achievement lower than the criterion (50 percent).
This research aimed to: firstly, find out the effectiveness of a developed online interactive learning in enhancing English in daily life towards ASEAN for hearing-impaired students at the lower secondary level. Secondly, to determine the learning achievement of the students who used the online interactive learning. Thirdly, to calculate the effectiveness index of the online interactive learning in enhancing English for the hearing-impaired students. Fourthly, to examine the relationship between learning avidity scores and learning achievement of the hearing-impaired students who used the online interactive learning. The research utilized a one-group pre-test and post-test design. The sample consisted of 35 hearing-impaired students at the lower secondary level from the School for the Deaf in Surin Province, who expressed interest and readiness. The data was collected during the first semester of the academic year 2022. The research employed four tools: online interactive learning related to English in daily life towards ASEAN, pre-test, post-test, and learning avidity scores based on the eight desirable characteristics of students. The data analysis involved the use of percentage, effectiveness index, dependent t-test, and Pearson's Product Correlation Coefficient. The research findings indicated that online interactive learning enhanced knowledge of English in everyday life towards ASEAN, with an efficiency rating of 1.82 according to the Meguigans’ criterion. Similarly, the learners' learning achievement after using online interactive learning was higher compared to before learning, with a statistical significance level of 0.05. The effectiveness index of online interactive learning was found to be 0.3139, indicating that the learners experienced a learning progress of 31.39 percent, which did not align with the set assumptions (more than 0.80 or 80 percent). Furthermore, the study revealed that the learning avidity scores and learning achievement of the learners were significantly correlated at the 0.05 level.
Scholarship applicants' interviews generally last from an hour to an entire day. Several critical situations of each student caused the interviewer's feelings, such as stress, sadness, and anxiety. Listening to and discussing negative points can cause mental health problems. In order to decrease the duration of interviewing, This research paper discusses the development of a machine learning model for evaluating Student Loan Fund interview scores and an automated voice-based interactive machine learning interview scoring system. The process consists of building a machine-learning model using the Random Forest Algorithm with an accuracy of 99.54% and the SMOTE method to adjust the data asymmetry. The software was developed as a web application based on the SDLC for a second process, running Cloud Speech-to-Text of Google Speech API. Experts in all five areas evaluated the software, and the evaluation score was high. The research also looks at the Software Development Life Cycle (SDLC) models and the use of cross-validation and bootstrapping for accuracy estimation and model selection.
Human action recognition has a widespread application in medical health, human-machine interaction, sports competition and similar.In this study, the authors used the literature review method to introduce the standard sensors and corresponding methods used for motion data collection and briefly described the general techniques for motion data preprocessing.Also, introduced the process for extracting features of motion data and pointed out the research status of standard feature recognition methods as well as briefly described their application status.Finally, the current problems related to the recognition features were discussed as well as those related to future development.The future development may focus on hardware system optimization, model optimization and personalized recognition.
Remote Procedure Call (RPC) is defined as a model for inter-process communication in distributed systems.RPC is simple as well as powerful.However, most of the RPC systems used synchronously in nature and synchronous RPC systems are not able to exploit fully parallelism in distributed applications.Therefore, various asynchronous RPC systems have been designed to achieve higher parallelism while retaining the simplicity of synchronous RPC.Asynchronous RPC calls do not block the client and the replies can be received when they are needed.Asynchronous RPC calls can be categorized into two types depending on whether the calls return a value.Most asynchronous RPC systems only support calls that do not return a value.An analysis and comparison of various asynchronous RPC systems were presented in this paper.Among several asynchronous RPC interfaces, the Mercury interface seems to be appropriate to use in high-performance computing systems where a large volume of data needs to be transferred.
