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Leveraging Data Analytics Across Digital Product Development Stages: A Systematic Review and Conceptual Framework
(2026-06-01)
Alamsyah, Noor
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Thanasopon, Bundit
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Jamsri, Pornsuree
Background: Data analytics (DA) is a field that has expanded greatly and is an important tool for digital product development, and has captured researcher and practitioner interest. Nevertheless, from an Information Systems and Business Intelligence (IS/BI) view, there is still a lack of knowledge regarding the role that data analytics plays in the digital product development lifecycle for decision making. The volume and the complexity of digital product innovation and analytics continues to increase, thereby further enhancing the need for existing knowledge to be consolidated in this area. Objective: This research systematically reviewed the latest academic research in the field of data analytics in digital product development and explain the specific uses of data analytics in the different stages of digital product development for supporting decision making and innovation activities. Methods: This study followed the systematic literature review method through ScienceDirect, IEEE Xplore and Emerald databases. Upon initial search, 1,554 articles were found; 33 relevant articles were identified after a structured screening and eligibility assessment of the articles in line with Kitchenham's protocol. Results: The results reveal the differentiated use of data analytics in the various stages, namely opportunity identification through text mining, feasibility assessment through predictive modelling, prototyping through digital twins and generative design and market responsiveness through predictive analytics and recommender systems. Even with analytical processes and concepts in place, companies often face challenges due to data integration issues, analytical skill, and organizational preparedness. The data quality issues, algorithmic bias, ethical considerations, or lack of algorithm transparency all contribute to these limitations, hindering the full potential of data analytics in digital product development. Conclusion: To realize more effective and aligned outcomes of innovation, it is important to understand how data analytics can assist in decision making throughout the digital product development lifecycle. This research is valuable for researchers and practitioners as it provides a structured conceptual framework for association of analytics initiatives with digital product development goals. There is potential for this work to be extended in future studies, involving the creation and validation of scalable analytics frameworks in various organisational contexts.
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Comparative Analysis of ANN and ANFIS-Based MPPT for Photovoltaic System
(2025-01-01)
Bi, Chenxing
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Jiriwibhakorn, Somchat
Eco-friendly and sustainable energy sources, such as solar and wind power, have rapidly integrated into the grid in recent years. Solar power generation is influenced by factors such as radiation intensity and temperature. However, the intermittent nature of solar energy poses challenges for consistent power generation. Intelligent techniques are essential for maximizing power extraction. This study focuses on developing Artificial Intelligence-based Maximum Power Point Tracking (MPPT) methods in solar photovoltaic (PV) systems. Two AI techniques were implemented, and their performance was compared to achieve more efficient results. Real-time data from micro-nano grids in China were utilized to design a feed-forward neural network and an adaptive neural network-based MPPT system. These systems aim to optimize solar energy harvesting and enable integration into nano-grid systems, offering affordable and eco-friendly energy solutions for families in Thailand. The algorithms were developed using MATLAB/Simulink, and both techniques demonstrated excellent performance. Notably, the ANFIS-based MPPT system outperformed, achieving a tracking efficiency of 93% in PV systems.
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Deep context-attentive transformer transfer learning for ﬁnancial forecasting
(2025-01-01)
Feng, Ling
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Sinchai, Ananta
This study presents 2CAT (CNN-Correlation-based Attention Transformer), a deep learning model for financial time-series forecasting. The model integrates signal decomposition, convolutional layers, and correlation-based attention mechanisms to capture temporal patterns. A transfer learning framework is incorporated to enhance generalization across markets through pretraining, encoder freezing, and fine-tuning. Evaluation on six stock indices—Dow Jones Industrial Average (DJIA), Nikkei 225 (N225), Hang Seng Index (HSI), Shanghai Stock Exchange (SSE), Bombay Stock Exchange (BSE), and the Stock Exchange of Thailand (SET)—demonstrates strong predictive accuracy. On DJIA, 2CAT records an MSE of 0.0655, MAE of 0.2023, and R2 of 0.9169, outperforming Deep-Transformer, which yields an MSE of 0.1360 and R2 of 0.8274. The SET index, which posed challenges for previous models, demonstrates notable improvement with 2CAT, achieving an R2 of 0.9094. Wilcoxon signed-rank test confirms statistically significant gains in non-transfer learning scenarios at the 0.05 level. Transfer learning experiments reveal statistically significant improvements, reinforcing the feasibility of cross-market knowledge transfer. An ablation study highlights the impact of architectural refinements and rotary positional encoding, while prediction horizon analysis confirms stable forecasting performance. These results establish 2CAT as a robust financial forecasting framework adaptable to diverse market conditions.
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Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation
(2024-06-01)
Jitkajornwanich, Kulsawasd
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Vijaranakul, Nattadet
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Jaiyen, Saichon
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Srestasathiern, Panu
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Lawawirojwong, Siam
Due to climate change, the air pollution problem has become more and more prominent [23]. Air pollution has impacts on people globally, and is considered one of the leading risk factors for premature death worldwide; it was ranked as number 4 according to the website [24]. A study, ‘The Global Burden of Disease,’ reported 4,506,193 deaths were caused by outdoor air pollution in 2019 [22,25]. The air pollution problem is become even more apparent when it comes to developing countries [22], including Thailand, which is considered one of the developing countries [26]. In this research, we focus and analyze the air pollution in Thailand, which has the annual average PM2.5 (particulate matter 2.5) concentration falls in between 15 and 25, classified as the interim target 2 by 2021′s WHO AQG (World Health Organization's Air Quality Guidelines) [27]. (The interim targets refer to areas where the air pollutants concentration is high, with 1 being the highest concentration and decreasing down to 4 [27,28]). However, the methodology proposed here can also be adopted in other areas as well. During the winter in Thailand, Bangkok and its surrounding metroplex have been facing the issue of air pollution (e.g., PM2.5) every year. Currently, air quality measurement is done by simply implementing physical air quality measurement devices at designated—but limited number of locations. In this work, we propose a method that allows us to estimate the Air Quality Index (AQI) on a larger scale by utilizing Landsat 8 images with machine learning techniques. We propose and compare hybrid models with pure regression models to enhance AQI prediction based on satellite images. Our hybrid model consists of two parts as follows: • The classification part and the estimation part, whereas the pure regressor model consists of only one part, which is a pure regression model for AQI estimation. • The two parts of the hybrid model work hand in hand such that the classification part classifies data points into each class of air quality standard, which is then passed to the estimation part to estimate the final AQI. From our experiments, after considering all factors and comparing their performances, we conclude that the hybrid model has a slightly better performance than the pure regressor model, although both models can achieve a generally minimum R<sup>2</sup> (R<sup>2</sup> > 0.7). We also introduced and tested an additional factor, DOY (day of year), and incorporated it into our model. Additional experiments with similar approaches are also performed and compared. And, the results also show that our hybrid model outperform them. Keywords: climate change, air pollution, air quality assessment, air quality index, AQI, machine learning, AI, Landsat 8, satellite imagery analysis, environmental data analysis, natural disaster monitoring and management, crisis and disaster management and communication.
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Artificial Intelligence Enhancing Physical Education: A systematic literature review
(2024-01-01)
Zhao, Juan
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Sitthiworachart, Jirarat
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Ratanaolarn, Thanin
This study aims to systematically review the application of Artificial intelligence (AI) technology to enhance physical education (PE). To this end, 104 journals in the databases of Baidu Scholar, Google Scholar, and King Mongkut's Institute of Technology Ladkrabang Library from 2014 to October 2023 were selected as research objects, and the research results were analyzed using the content analysis method. The analysis results show that the first research on this topic was conducted in 2014, and the Number of relevant studies has increased yearly, especially during the pandemic. In addition, these studies were mainly conducted in a combination of quantitative and qualitative, mainly journal articles. In addition, the samples of these studies are from each academic section, and most of the studies are conducted by college students. The analysis focuses on article distribution, publication time, research content, technology selection, and current and future challenges and summarizes the development trend, specific applications, benefits, and challenges of AI sports. The findings of the studies revealed that AI technology in PE can improve students' physical achievement, cognition, and learning habits. On the other hand, AI technology can enhance the effect of PE so that students have a greater interest in learning sports. Finally, some suggestions for future research are put forward.
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U-GMo: Individual Clip Detection from a Graduation Ceremony Video
(2024-01-01)
Treesoonrat, Natee
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Kriengchaiyaprug, Nunnapat
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Upadhayawong, Thanakann
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Lohapongpan, Warinya
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Chawuthai, Rathachai
Graduation ceremonies are important occasions in life. A video in this event is usually very long due to a lot of graduates getting their degree. This study suggests a method for automatically cutting the entire ceremony video into customized segments that only include the most significant events for each particular graduate, named U-GMo (Your Great Moment). The system uses deep learning with computer vision techniques, such as YOLOv8 for posture detection, to identify graduates by observing their motions and posture during the degree ceremony. After that, the identified bits are taken out and assembled into brief video snippets for every graduate. The algorithm can detect and extract each graduate's crucial moments with high performance, according to an examination conducted on a dataset of graduation ceremonies. The personalized video clips provide a convenient way to preserve the meaningful highlights from these milestone events.
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AI-enabled Exit Strategy of Emergency Vehicle Preemption
(2023-01-01)
Khoirunnisaa, Kunti
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Hartanto, Rudy
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Wayan Mustika, I.
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Woraratpanya, Kuntpong
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Arva Arshella, Ika
Emergency Vehicle Preemption (EVP) is a system employed by traffic lights to prioritize emergency vehicles, such as ambulances, fire engines, and police cars. This technology allows these vehicles to safely pass through crowded intersections by stopping traffic from other directions. However, it is essential to consider that preempting the regular traffic signal control could have implications for the safety and efficiency of both the prioritized emergency vehicle and the overall flow of regular traffic at the intersection. Research on EVP and Exit Strategy has been extensively developed and implemented in various commercial products, including the Trafficware 980 ATC V76 signal controller. Trafficware offers multiple EVP schemes and Exit Strategy modes that can be customized by traffic operators to suit their specific requirements. Studies have indicated that certain Exit Strategies perform optimally under specific traffic conditions. Yet, the complexity of traffic conditions poses a challenge for traffic operators to optimize the Trafficware's performance by selecting the most effective Exit Strategy mode. To address this, Artificial Intelligence (AI) with Reinforcement Learning (RL) techniques used to improve the Trafficware 980 ATC V76 system. Particularly, the Exit to Fixed Phase strategy will be optimized, enabling Trafficware to dynamically choose the most suitable exit phase from the available four phases based on real-time traffic conditions. Implementing this optimization will replace the current fixed exit phase selection with a more adaptable and responsive approach, leading to improved traffic management and efficiency, ultimately resulting in reduced waiting times. The results show that the AI-enabled exit strategy can reduce the waiting time with the best performance up to 7.8% compared to no exit strategy. The Trafficware 980 ATC V76 has been upgraded with the ability to adjust signals based on policies to recover from congestion caused by preemption.
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Research on Informatization Education Methods under the Background of Artificial Intelligence
(2021-06-01)
Zhufeng, Ye
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Sitthiworachart, Jirarat
Higher education reform has entered a critical period, while the impact of artificial intelligence on higher education is also deepening. This paper studies the internal elements of higher education in this context: the development and reform of higher education education forms, curriculum construction and planning, classroom teaching forms, teacher- student relations, class organization and management, etc.; discusses the development and transformation of artificial intelligence from national strategic planning The process of moving towards higher education practice; seeking reform paths for higher education, management, teaching and artificial intelligence technology and social changes in the new era.
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Recurrent neural network based underground object detection using a-scan ground penetrating radar
(2021-05-19)
Choochim, Poomsak
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Kasjarun, Panut
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Phasukkit, Pattarapong
Ground penetrating radar is a highly effective tool to find objects buried underground. But there are still some limitations that prevent it from being applied to a wider variety of applications. The complexity of processing and the opportunity to collect the signal. This work is applied GPR with AI to reduce the processing steps and displayed your object's features immediately. Based on the time series model in Bidirectional Neural Network as RNN in bidirectional format. Because it possible to predict the position and shape of the object simultaneously. The object was buried under a sandbox with a depth of 10 - 50 centimeters and used two antennas to receive and transmit the signal as a GPR machine in this experiment. Found that the accuracy is at 92.8 % and also measured by F1-score in each target positions as well, when using the results of this work to add features and extend it to work with multiple functions, it will be possible to use the GPR used with Neural Network model can be used in the actual situation.
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Emergence of new business environment with big data and artificial intelligence
(2019-08-23)
Chaveesuk, Singha
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Khalid, Bilal
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Chaiyasoonthorn, Wornchanok
The research paper aims to study the factors related to incorporation of the artificial intelligence and big data in the business environment leading to rapid transformation leading to businesses expansion while riding the market trends, taking over of the competitions and other dynamics of the business environment. The need of conducting this research evolved from the recent events which have put many data mining companies under fire with the uncovering of the Big-Data scandals, manipulating and directing consumers’ mindsets among masses to affect the favorable impressions. Furthermore, it has been also observed that business tends to seek assistance through its professionals, corporate and social networks to have price parity to achieve its strategic business goals and objectives.

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