Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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This Author published in this journals
All Journal Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Thosporn Sangsawang
Rajamangala University of Technology Thanyaburi, Thailand
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Predicting Ad Click-Through Rates in Digital Marketing with Support Vector Machines Thosporn Sangsawang
Journal of Digital Market and Digital Currency Vol. 1 No. 3 (2024): Regular Issue December 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i3.20

Abstract

This study investigates the effectiveness of Support Vector Machines (SVM) in predicting click-through rates (CTR) in digital marketing campaigns. Utilizing a dataset comprising user demographic and behavioral data, the research aims to develop a predictive model to forecast ad clicks accurately. The primary objectives include conducting exploratory data analysis (EDA), preprocessing data, training the SVM model, and evaluating its performance using standard metrics. The dataset includes features such as Daily Time Spent on Site, Age, Area Income, Daily Internet Usage, and Gender. Key findings from the EDA reveal that "Daily Time Spent on Site" and "Daily Internet Usage" are significant predictors of CTR, with notable correlations. The SVM model, trained on this data, demonstrated exceptional performance, achieving an accuracy of 97.65%, a precision of 98.58%, a recall of 96.53%, and an F1-score of 97.54%. These results confirm the model's robustness and reliability, indicating its potential for optimizing digital marketing strategies. The study's significance lies in its contribution to the fields of digital marketing and predictive analytics by showcasing the applicability and advantages of SVM in predicting user behavior. These insights can help marketers optimize ad placements, enhance user engagement, and improve return on investment. Practical implications include strategies for targeted and personalized marketing based on key user demographics and behaviors. Despite the promising results, the study acknowledges limitations such as the dataset size and scope of features. Future research should focus on utilizing larger and more diverse datasets, incorporating additional features, and exploring other advanced machine learning algorithms. This research encourages further exploration of machine learning applications in digital marketing to enhance predictive accuracy and campaign effectiveness. By addressing these aspects, this study aims to advance the academic understanding and practical implementation of predictive analytics in digital marketing, providing a robust framework for future applications.
Investigating the Determinants of NFT Purchase Intention: An Integrated Model Combining the Theory of Planned Behavior and Technology Acceptance Model Thosporn Sangsawang
Journal of Current Research in Blockchain Vol. 1 No. 3 (2024): Regular Issue December 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i3.19

Abstract

This study investigated the determinants of Non-Fungible Token (NFT) purchase intention (PI) by integrating the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). The research aimed to identify key factors influencing PI), including Attitude Toward NFTs (AT), Perceived Usefulness (PU), Perceived Ease of Use (PEU), Subjective Norms (SN), Perceived Behavioral Control (PBC), and Perceived Risk (PR). A quantitative research design was employed, with data collected through an online survey distributed via Google Forms in February 2024. Out of the 345 questionnaires initially distributed, 336 were validated and included in the analysis after filtering for participants with actual NFT usage experience. The findings revealed that PU and PEU positively influenced AT, which in turn significantly enhanced PI. PBC and SN were also found to have direct positive effects on PI, highlighting the importance of consumer confidence and social influence in driving behavior. Conversely, PR demonstrated a negative impact on PI, underscoring the deterrent effects of concerns related to security, privacy, and financial uncertainty. The study further confirmed the mediating role of attitude, showing that positive evaluations of NFTs play a crucial role in translating perceived benefits and usability into actionable PI. The integrated model combining TPB and TAM effectively explained the complexities of NFT PI, offering valuable insights for both theoretical understanding and practical applications in the NFT market. These results provide actionable recommendations for NFT platforms and marketers to enhance user engagement, mitigate PR, and foster positive consumer AT.
Automated Identification of Gait Anomalies Using Deep Autoencoder and Isolation Forest for Hybrid Anomaly Detection Sangbum Kim; Thosporn Sangsawang
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.44

Abstract

Human gait analysis plays a vital role in assessing locomotor function, postural stability, and early detection of motor impairments. This study proposes an unsupervised hybrid anomaly detection framework that integrates PCA and Isolation Forest (IF) to automatically identify abnormal gait patterns using a Multivariate Biomechanical Dataset (MGAD) containing 5,000 gait samples. PCA was utilized to reduce dimensionality and compress correlated gait features while retaining 95.1% of the total variance, thereby preserving essential biomechanical information. The reconstruction errors obtained from PCA were subsequently analyzed using Isolation Forest to isolate anomalous gait instances. Experimental results demonstrate that the hybrid PCA–IF model effectively differentiates between normal and abnormal gait behaviors, achieving an ROC-AUC of 0.912 and an F1-score of 0.866, indicating strong discriminative capability and model stability. Feature-level reconstruction analysis revealed that stance phase duration, step length, and stride length are the most influential determinants of gait irregularities, aligning with established clinical findings in gait biomechanics. The proposed framework is computationally efficient, interpretable, and fully unsupervised, making it suitable for real-time clinical assessment, rehabilitation monitoring, and wearable healthcare applications. These findings highlight the potential of hybrid statistical–machine learning models in advancing automated gait diagnostics and intelligent mobility analytics.
Co-Authors Sangbum Kim