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An AI-integrated framework for sustainable conflict mitigation and policy innovation
(2026-09-01)
Tissamana, Apinya
;
Ajayi, Babatunde Oluwaseun
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Bamisaye, Mayowa Emmanuel
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Katerenchuk, Wendell
;
Aziz, Tamoor
Conflict in Thailand's peripheral regions remains a persistent challenge, particularly in provinces such as Ubon Ratchathani, where inequalities, cultural marginalization, and resource pressures intersect to shape local tensions. Despite growing attention to conflict analysis, existing approaches often struggle to combine contextual understanding with predictive capability, limiting their usefulness for timely and effective policy intervention. To address this gap, this research introduces a hybrid framework integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method with the Random Forest (RF) algorithm. Using primary data from 400 respondents across urban and rural communities in Ubon Ratchathani, the framework combines stakeholder-informed causal mapping with data-driven prediction. DEMATEL identifies and structures relationships among key conflict dimensions, while RF evaluates and predicts these relationships using empirical data. The findings reveal that cultural and identity issues are the primary drivers of conflict, shaping political and economic marginalization, whereas resource-related conflicts emerge as downstream effects. The RF model demonstrates excellent predictive performance (MSE = 0.0120; RMSE = 0.1097), indicating that these relationships can be reliably translated into predictive insights. This enables policymakers to move beyond reactive responses toward early identification of conflict risks and more targeted interventions. Effective interventions, however, must be coordinated across sectors and tailored to local contexts to achieve lasting impacts. By linking causal understanding with predictive capability, the proposed framework offers a practical tool for conflict monitoring and more inclusive governance aligned with the Sustainable Development Goals (SDGs). While promising, the framework should be tested in other regions to assess its broader applicability.
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Anomaly Flying Height Prediction Based on Clustering Techniques in Hard Disk Drive Manufacturing
(2025-01-01)
Kanjanapruthipong, Worawit
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Konghuayrob, Poom
In this research, we present a method for predicting anomaly flying height (FH) profiles in hard disk drive (HDD) manufacturing by analyzing FH data at the FH1 stage. Anomalies at FH1 can lead to calibration issues at FH2, disrupting the production process. We propose an AI-based approach using unsupervised clustering techniques to group FH profiles of the read/write head. We evaluated four clustering algorithms, KMeans, MiniBatchKMeans, Birch, and BisectingKMeans, along with the Elbow method to determine the optimal number of clusters. By identifying anomalous FH profiles early at FH1, the method enables proactive intervention, reducing calibration process time and improving production efficiency. Our model achieved an accuracy of 0.939 without relying on manual feature selection (e.g., pressure and temperature), which is often difficult to capture using traditional linear or rule-based models owing to the nonlinear nature of FH profiles. These results demonstrate the practical potential of clustering techniques in enhancing HDD manufacturing processes.
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Prediction of Flying Height Using Deep Neural Network Based on Particle Swarm Optimization in Hard Disk Drive Manufacturing Process
(2024-01-01)
Kanjanapruthipong, Worawit
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Prasitmeeboon, Pitcha
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Konghuayrob, Poom
In contemporary hard disk drive (HDD) manufacturing processes, after the assembly of the HDD from the production line, a series of diverse calibration procedures are necessary to ensure standardization. These include capacity calibration, which determines the storage space in terabytes (TB) presently available, and flying height (FH) calibration, which evaluates the distance between the head and the disk by applying electric current to the heater coil element to achieve the desired FH, thus optimizing the writing and reading performance and tailoring it to each HDD. Additionally, electric current is saved in a digital-to-analog converter (DAC) unit for the utilization of a read/write head, while a preamp collaborates with the drive firmware to convert the electric current in the DAC unit to milliwatts. In the present scenario, multiple calibrations of flying heights (FHs), specifically flying height 1 (FH1) and flying height 2 (FH2), are performed. Each FH calibration requires a testing time of approximately 5 h owing to the separation of measurement points into 240 locations across the disk surface, referred to as test zones, with a total of 20 heads. The primary objective of this study is to reduce the testing time by using a combination of deep neural network (DNN) and particle swarm optimization techniques to predict the DAC profiles of FH2 as it approaches FH1, where FH1 is the input for the DNN model.
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The factors affecting the IoT user satisfaction in Sinyeong city, China
(2021-01-01)
Chen, Peizhi
;
Tangthong, Sorasak
With the development of Internet and information technology as well as the birth of the concept of smart cities, paramount changes occur in people's life. People nowadays can use various high-end smart devices at home without going out. Therefore, the construction of Internet of Things (IoT) facilities is particularly suitable for modern smart cities, which is not only convenient and reliable, but also greatly facilitates people's lives. This research aimed to study and investigate the factors affecting the IOT user satisfaction of citizen in Sinyeong City from China through a quantitative survey. After studying, investigating, and researching, the problems had also been discovered in the construction and application of the Internet of Things in Sinyeong City, as well as the various needs and demands of the citizens. Among them, the influencing factors include information quality, system quality and service quality. The implication which expected, can help government departments to formulate better policies that benefit the people's livelihood to provide the IoT for further smart city, to comprehensively improve the speed of national smart city construction.
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Experimental Study and Modeling of Automatic Home Energy Management System Using AI
(2021-01-01)
Pranee, Piyanut
;
Jirasuwankul, Nirudh
This paper proposes an experimental study and modeling of Fuzzy logic based-AI for home energy management system. The management model has been designed for home in the subtropical climate zone-like, i.e., Thailand, which having yearly and monthly average temperature of 28°c and 30-38°c in the hottest season respectively. The studied system model comprises of the grid-connected load of home appliances, air conditioner, type-1 EV charger and solar rooftop PV supply. The objective of energy management is to minimize grid power consuming as well as maximizing solar PV utilization with 24-hour load profile, principally running of air conditioner and EV charging load. By testing the proposed management system comparatively to the generic system without managing scheme, energy saving of 43.90% can be achieved under the same operating and environmental conditions. Those are illustrated by the simulation results.
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The effectiveness of RPA in fine-tuning tedious tasks
(2020-07-01)
Sutipitakwong, Sutipong
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Jamsri, Pornsuree
This research focuses on the usage and importance of RPA (robotic process automation) to fine-tune tedious work such as filling in forms of education workshops. The research looks at how RPA can be used to prepare learning documents and materials used by large numbers of participants. The study was conducted using an RPA to test cases with repetitive routines. It was proposed that the process of automated work management and AI would result in a more efficient and effective workflow while reducing the error margin. The research will show that RPA can manage a large capacity of documents with very high accuracy, which will be beneficial for educational workshops with many participants requiring a great deal of material preparation and exportation for analysis afterward.
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A rule-based training for artificial neural network packet filtering firewall
(2019-11-01)
Khunkitti, Akharin
;
Chongsujjatham, Ponsuda
The Artificial Neural Network has been used in many network applications, including firewalls. Training process of neural network is very important to define the intelligence of the systems. Many artificial neural network firewalls used direct network packets for training process, which may be difficult to get training samples and may not follow their firewall's policies. This research work proposes a rule-based training for artificial neural network packet filtering firewall. The developed neural network model is trained by generating samples from legacy firewall ruleset. Each rule has been converted to random training samples. All firewall's rules are used to generate the training sample data, rule by rule. The accuracy results show high accuracy with some behavior studies. The number of samples per rule, number of rules and rule style, including default rule and rule-scope effects, have been studied for the best accuracy results. This study also concludes the styles of firewall ruleset for the best accuracy of the proposed system.

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