Study of Career Education for Women: Development of Global Human Resources

Hori, Mayumi

doi:10.47738/ijaim.v1i2.9

Cited by 7 publications

(5 citation statements)

References 2 publications

(3 reference statements)

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“…Model creation involves the selection and implementation of machine learning algorithms for digit recognition [25], [26]. In this research, unsupervised learning techniques such as clustering, dimensionality reduction, and generative modeling are employed to extract meaningful representations from the MNIST dataset.…”

Section: Model Creationmentioning

confidence: 99%

Unsupervised Learning for MNIST with Exploratory Data Analysis for Digit Recognition

Hery

2024

J. Appl. Data Sci.

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This research investigates the application of unsupervised learning techniques for digit recognition using the MNIST dataset. Through a comparative analysis, various dimensionality reduction methods, including ISOmap, PCA, and tSNE, were evaluated for their effectiveness in visualizing and processing the MNIST data. The findings reveal that tSNE consistently outperforms ISOmap and PCA in terms of accuracy, F1score, precision, and recall, showcasing its superior capability in preserving relevant information within the dataset. Utilizing tSNE for visualizing and clustering digits provides valuable insights into the underlying structure of the dataset, uncovering complex patterns in digit relationships. These results contribute to the advancement of digit recognition systems, offering potential improvements in classification accuracy and model reliability. The success of tSNE highlights the importance of nonlinear dimensionality reduction techniques in handling complex datasets, such as MNIST. This research underscores the significance of unsupervised learning approaches, particularly tSNE, in enhancing digit recognition systems' performance, with implications extending across various application domains. Continued research is recommended to explore further applications and potentials of unsupervised learning techniques and to deepen our understanding of the MNIST dataset's structure and complexity.

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Section: Model Creationmentioning

confidence: 99%

Unsupervised Learning for MNIST with Exploratory Data Analysis for Digit Recognition

Hery

2024

J. Appl. Data Sci.

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“…Fourth Round: By this round, the feasible ideas had been identified, resolved and reported. The experts acknowledged all the group's opinions with the ideas or strategies and details of implementation [20,21]. Three education technology specialists were considered to customize the questionnaire to match the subject.…”

Section: Delphi Techniquementioning

confidence: 99%

Data Analytics of Online Lessons in Social Studies and Buddhism: Enhancing Dhamma Teaching and Tripitaka Understanding Among Teachers and Students

Luaensutthi

2023

J. Appl. Data Sci.

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The objectives were to (1) determine the effectiveness of online lessons of Social Studies and Buddhism on Dhamma's teaching regarding Tripitaka for teachers; (2) compare the pre-test and post-test achievements of teachers and primary school 6 (Grade 6) students; 3) examine the satisfaction of teachers and students using online lessons of Social Studies and Buddhism on Dharma's teachings according to the Tripitaka. The samples were 12 teachers, and 30 students studying primary school 6 (Grade 6) at Wat Proifon School. The instruments were online lessons of the Social Studies and Buddhism course on Buddha's Teaching Tripitaka, pre-test and post-test, and the questionnaire of teachers' and students' satisfaction towards studying the online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding the Tripitaka.Statistics used were percentage, mean, standard deviation, and t-test for dependent samples. The findings revealed that the efficiency of online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding Tripitaka was 81.92/80.83 on average based on the criteria. The teachers' learning achievements after using online lessons in the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test 11.40, SD.=1.51, while the average score of the post-test was 18.17, SD.=1.10, and the t-test between the pretest and post-tests was 6.77, which were significantly distinctive at the level of .05., and the students' learning achievements after using online lessons on the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test: 10.40, SD.=1.61, while the average score of the post-test was 16.17, SD.=1.11, and the t-test between the pre-test and post-tests was 5.77, which were significantly distinctive at the level of .05. Teachers' satisfaction was at high level with an average of 4.47, SD.=.55, and the students' satisfaction gained a very high level with an average of 4.50, SD.=.44.

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“…The advantage of the RNN algorithm lies in its ability to handle sequential data such as text or speech, producing reasonably accurate outcomes in recognizing these data patterns. However, it tends to overfit when the data are insufficient, and training the model can be time-consuming [34][35][36]. The experimental process is significant as it assists in identifying the most suitable algorithm for this research.…”

Section: Research Stepsmentioning

confidence: 99%

Exploring the Flexibility and Accuracy of Sentiment Scoring Models through a Hybrid KNN-RNN-CNN Algorithm and ChatGPT

Hariguna,

Ruangkanjanases

2023

HighTech. Innov. J.

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This study aimed to address the limitations of sentiment analysis by developing a more accurate and flexible sentiment scoring model using ChatGPT in combination with KNN, RNN, and CNN algorithms. To achieve this, primary data from ChatGPT and secondary data from Kaggle were utilized for training. The model's performance was evaluated, yielding an impressive accuracy rate of 88.17%. This research underscores ChatGPT's pivotal role in offering theoretical insights and precise data for diverse applications. The novelty of this study lies in its innovative approach of combining KNN, RNN, and CNN algorithms to create a more adaptable and accurate sentiment scoring model. Additionally, the primary data from ChatGPT greatly enhances the creation of precise and relevant training data across various topics and languages. Despite these achievements, there remains a need for further exploration of testing methods to mitigate the impact of data limitations on result generalizability. Moreover, it is acknowledged that the model's effectiveness may be diminished when applied to languages other than English. Nevertheless, this research provides a promising avenue for users seeking enhanced and precise sentiment analysis by integrating KNN, RNN, and CNN algorithms with ChatGPT. The findings of this study can serve as a solid foundation for future research endeavors in the advancement of sophisticated and effective sentiment analysis technologies. Doi: 10.28991/HIJ-2023-04-02-06 Full Text: PDF

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Study of Career Education for Women: Development of Global Human Resources

Cited by 7 publications

References 2 publications

Unsupervised Learning for MNIST with Exploratory Data Analysis for Digit Recognition

Unsupervised Learning for MNIST with Exploratory Data Analysis for Digit Recognition

Data Analytics of Online Lessons in Social Studies and Buddhism: Enhancing Dhamma Teaching and Tripitaka Understanding Among Teachers and Students

Exploring the Flexibility and Accuracy of Sentiment Scoring Models through a Hybrid KNN-RNN-CNN Algorithm and ChatGPT

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