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检索条件"机构=Research Center for Academic Excellence in Nonlinear Analysis and Optimization"

共 17 条记录，以下是1-10 订阅

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Mitigating Data Sparsity in Point-of-Interest Recommendation Systems Using Multi-Modal User Profiles 10

Mitigating Data Sparsity in Point-of-Interest Recommendation...

引用

10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Arthan, Sudarat Tamee, Kreangsak Naresuan University Department of Computer Science and Information Technology Phitsanulok Thailand Naresuan University Research Center for Academic Excellence in Nonlinear Analysis and Optimization Naresuan University Department of Computer Science and Information Technology Phitsanulok Thailand

ISBN: (纸本)9798331543273

This study presents a solution to the data sparsity issue in POI recommendation systems by introducing a multi-modal user profile approach called Visual-Textual Collaborative Filtering (VTCF). VTCF integrates textual POI descriptions from Wikipedia with user-generated photos to improve the representation of user preferences. Using publicly available geo-tagged photo datasets, we compared VTCF with various baseline methods, including traditional user-based collaborative filtering and approaches based on visual and contextual preferences. The experimental results demonstrate that our approach successfully addresses the challenges of sparse check-in data, resulting in more accurate and relevant recommendations. The findings suggest that VTCF is a promising method for improving recommendation accuracy. Future work could explore additional data sources and techniques to further enhance POI recommendation systems. Moreover, computational trade-offs and scalability should be considered, as integrating textual and visual data adds system complexity. Strategies such as batch processing and parallel computation could help mitigate these issues for large-scale applications. © 2025 IEEE.

关键词： Recommender systems

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Mitigating Data Sparsity in Point-of-Interest Recommendation Systems Using Multi-Modal User Profiles

Mitigating Data Sparsity in Point-of-Interest Recommendation...

引用

International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Sudarat Arthan Kreangsak Tamee Department of Computer Science and Information Technology Naresuan University Phitsanulok Thailand Department of Computer Science and Information Technology Naresuan University Research Center for Academic Excellence in Nonlinear Analysis and Optimization Naresuan University Phitsanulok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

关键词： Visualization Accuracy Collaborative filtering Soft sensors Scalability Batch production systems Robustness Complexity theory Resource management Recommender systems

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Increasing Efficiency in Counting Number of Motorcycle with Object Relation Matching 7

Increasing Efficiency in Counting Number of Motorcycle with ...

引用

7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2022

作者： Tongmonwit, Boonyakon Tamee, Kreangsak Naresuan University Faculty of Science Department of Computer Science and Information Technology Phitsanulok65000 Thailand Naresuan University Research Center for Academic Excellence in Nonlinear Analysis and Optimization Faculty of Science Department of Computer Science and Information Technology Phitsanulok65000 Thailand

ISBN: (纸本)9781665495103

Counting number of object is very important for data collection. This research had experimented in counting number of motorcycle with object detection and tracking technology by using YOLOv4 model for object detection and Deepsort model for object tracking. However, the experiment showed several errors in counting number of object detection and tracking. Therefore, the object relation matching concept was applied in an attempt to solve these errors. It was found that, after applying object relation matching concept, precision in counting number of object rose from 68.29 percent to 80.99 percent, or 12.7 percent increase © 2022 IEEE.

关键词： Object detection

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The development of an internet of things mobile application for tracking an electric bus in a campus 4

The development of an internet of things mobile application ...

引用

4th Joint International Conference on Digital Arts, Media and Technology and 2nd ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT-NCON 2019

作者： Wongthai, Winai Khruahong, Sanya Srithong, Pasathon Samphao-Ngoen, Muthita Department of Computer Science and Information Technology and Research Center for Academic Excellence in Nonlinear Analysis and Optimization Faculty of Science Naresuan University Phitsanulok Thailand Chainatpittayakom School Chi Nat Thailand

ISBN: (纸本)9781538680728

To reach the idea of data science for the Internet of Things (IoT) innovation, we believe that if we can collect data from our daily life activities such as our commuting data from a bus system based on IoT. We can add values to this collected data using, for example, machine learning or data analysis and statistics. However, we need to enable the system to be able to measure our daily life's activities. As an example of such a system, this research demonstrates how to apply the IoT technology to track an electrical bus status of a mass transit system in a university. We develop a mobile application to show the information of the bus on a smartphone. The information can be alerted to a user or passenger in real-time. This information includes an estimated waiting time until the bus reaches a user, the number of passengers on board, and the current bus's position on the route in Google Maps. The mobile application can help a passenger to decide and manage the right journeys to travel in the university. The application was developed on the Android platform with many technologies including an Arduino integrated development environment or IDE, Google Maps application programming interface or API, a global positioning system or GPS module, ThingSpeak cloud, and infrared distance sensors. The 30 passengers evaluated the satisfaction survey. The survey mainly includes the accuracy of the tracking and alerting processes. The results are that the passengers were delighted with the processes. The evaluation related values are following. The mean is 4.91, and the standard deviation or SD is 0.29 or representing as 98.20%. To sum up, the application in this research can provide an example of mobile application development with current and available technologies such as IoT and cloud in the disruptive era to enhance applications in a transportation or others domain. © 2019 IEEE.

关键词： Internet of things

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Improving model using estimate error for daily inflow forecasting 4

Improving model using estimate error for daily inflow foreca...

引用

作者： Ngamsanroaj, Yaowalak Polsawang, Kantamat Tamee, Kreangsak Department of Computer Science and Information Technology Research Center for Academic Excellence in Nonlinear Analysis and Optimization Naresuan University Faculty of Science Phitsanulok65000 Thailand Beam Solution Co. LTD Bangkok10210 Thailand

ISBN: (纸本)9781538680728

Inflow forecasting is one of the important components for reservoir operation and resource management. To obtain enhanced accuracy for forecasting reservoir inflow, this paper proposed an improved model for forecasting the inflow of Bhumibol reservoir. The 3,169 records of daily inflow data from June 1, 2008, to February 1, 2017, had been collected to calculate the inflow into the reservoir by using Artificial Neural Networks (ANN) Back-Propagation Learning Algorithm for forecasting the inflow of the reservoir in the main model and error prediction model. The performance of the model can be evaluated by four methods: the coefficient of determination (R2), the Nash-Sutcliffe efficiency (NSE), the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). Thus, these proposed main and error prediction models were combined to support the forecast of reservoir inflow. The performance of the proposed model can be evaluated by following measured values: R2 was 0.927, NSE was 0.925, RMSE was 6.805 and MAE was 3.611. This indicates that the improved model provides more accurate value. © 2019 IEEE.

关键词： Neural networks

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An enhancement of an automatic plant watering system 22

An enhancement of an automatic plant watering system

引用

22nd International Computer Science and Engineering Conference, ICSEC 2018

作者： Wongthai, Winai Chanmee, Sirichanya Lohawet, Sampan Research Center for Academic Excellence in Nonlinear Analysis and Optimization Naresuan University Phitsanulok Thailand Faculty of Science Naresuan University Phitsanulok Thailand

ISBN: (纸本)9781538681640

We summarize of significant functions of the systems from previous works which are considered as automatic plant watering systems. As an example of an enhancement of the existing automatic plant watering systems, we design and implement a prototype of our proposed system based on these functions. Then we provide some discussions of the proposed system and its results. The system can demonstrate the enhancement of automatic plant watering systems and how the Internet of Things and cloud computing can benefit automatic plant watering systems. Thus the enhanced system can be useful in agricultural applications with plain design and implementation. The enhanced system can be guidelines for individuals who want to build their own automatic plant watering systems to grow plants, especially for food by themselves. © 2018 IEEE.

关键词： Cloud computing

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Very short-term photovoltaic power forecasting considering the stochastic factors 4

Very short-term photovoltaic power forecasting considering t...

引用

作者： Khumma, Kriangkamon Chaiwong, Khomyuth Tamee, Kreangsak Department of Computer Science and Information Technology Naresuan University Faculty of Science Phitsanulok65000 Thailand Department of Electrical and Electronics Engineering Loei Rajabhat University Faculty of Industrial Technology Loei42000 Thailand Department of Computer Science and Information Technology and Research Center for Academic Excellence in Nonlinear Analysis and Optimization Naresuan University Faculty of Science Phitsanulok65000 Thailand

ISBN: (纸本)9781538680728

This paper proposes photovoltaic (PV) power forecasting model, which is applied Gaussian blur algorithm filtering technique, for estimating power output and create stochastic forecasting model. As a result, affected power can be forecasted from stochastic factors with machine learning and artificial neural network. This work focuses on a very short-term forecast for 5 minutes ahead. As it uses only endogenous data, no exogenous data is needed. To evaluate the model, the result has been compared to the persistence model, which has a good accuracy for a short-term forecasting. It has been shown that the proposed PV forecasting considering the stochastic factors gained higher accuracy than the persistence model. © 2019 IEEE.

关键词： Neural networks

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Intelligence planning for aerobic training using a genetic algorithm 11th

Intelligence planning for aerobic training using a genetic a...

引用

11th International Symposium on Natural Language Processing, SNLP-2016 and 1st Workshop in Intelligent Informatics and Smart Technology, 2016

作者： Kumyaito, Nattapon Tamee, Kreangsak Research Center for Academic Excellence in Nonlinear Analysis and Optimization Faculty of Science Naresuan University Phitsanulok Thailand Department of Computer Science and Information Technology Faculty of Science Naresuan University Phitsanulok Thailand

ISBN: (纸本)9783319700151

A training plan is an important part of aerobic training. A training plan with a good sequence of high intensive training sessions and low intensive training sessions will substantially raise athletic performance. A creation of training plan require a sport scientist or a sports coach to do. An athlete who trains with limit in sports science knowledge may get injury. In this study, we propose a systemic implementation using a genetic algorithm (GA) to find optimal training plan. Comparison of this study result and an independently created, apparently reliable training plan, it reveal that GA is obtain capability to find optimal training plan. © Springer International Publishing AG 2018.

关键词： Genetic algorithms

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Improving model using estimate error for daily inflow forecasting

Improving model using estimate error for daily inflow foreca...

引用

International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Yaowalak Ngamsanroaj Kantamat Polsawang Kreangsak Tamee Department of Computer Science and Information Technology Naresuan University Phitsanulok Thailand Beam Solution Co. LTD Bangkok Thailand Department of Computer Science and Information Technology and Research Center for Academic Excellence in Nonlinear Analysis and Optimization Naresuan University Phitsanulok Thailand

Inflow forecasting is one of the important components for reservoir operation and resource management. To obtain enhanced accuracy for forecasting reservoir inflow, this paper proposed an improved model for forecasting the inflow of Bhumibol reservoir. The 3,169 records of daily inflow data from June 1, 2008, to February 1, 2017, had been collected to calculate the inflow into the reservoir by using Artificial Neural Networks (ANN) Back-Propagation Learning Algorithm for forecasting the inflow of the reservoir in the main model and error prediction model. The performance of the model can be evaluated by four methods: the coefficient of determination (R 2 ), the Nash-Sutcliffe efficiency (NSE), the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). Thus, these proposed main and error prediction models were combined to support the forecast of reservoir inflow. The performance of the proposed model can be evaluated by following measured values: R 2 was 0.927, NSE was 0.925, RMSE was 6.805 and MAE was 3.611. This indicates that the improved model provides more accurate value.

关键词： Predictive models Reservoirs Mathematical model Forecasting Data models Neural networks

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The Development of an Internet of Things Mobile Application for Tracking an Electric Bus in a Campus

The Development of an Internet of Things Mobile Application ...

引用

International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Winai Wongthai Sanya Khruahong Pasathon Srithong Muthita Samphao-ngoen Department of Computer Science and Information Technology and Research Center for Academic Excellence in Nonlinear Analysis and Optimization Faculty of Science Naresuan University Phitsanulok Thailand Department of Computer Science and Information Technology Faculty of Science Naresuan University Phitsanulok Thailand Chainatpittayakom School Chi Nat Thailand