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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Transformative Sport science Using Social Media as a Collaboration Tool 7

Transformative Sport Science Using Social Media as a Collabo...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Syahir, Arsyan Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Sports science can have a significant and positive impact on coaches and athletes, as well as for the professional sports organization. According to this situation, more and more organizations are competing to implement sports science as the monitoring systems. Along with the development of technology, it becomes easier for humans to carry out their activities, including communication with each other. Advances in the field of communication technologies have led to the use of social networks to support all activity. With social media, all kinds of information that people want to search for can be found easily. Social networks have their uniqueness in their characteristics. Like LINE has free calling and video calling features, while Instagram has features like Instagram Story, where users can document their activities in the form of photos or recordings. By all the characteristics of social networks, it will support the activity of sports science. The methodology of this research focused on quantitative research based on the T-test approach. The result shows that social media involvement can significantly support collaboration in sports science, so it can increase people's motivation to interact with other participants in sport organizations. © 2020 IEEE.

关键词： Sports

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Detection and Recognition of Vehicle Components Using Image Processing and Deep Learning Techniques

Detection and Recognition of Vehicle Components Using Image ...

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Research, Invention, and Innovation Congress (RI2C)

作者： Mahasak Ketcham Thittaporn Ganokratanaa Patiyuth Pramkeaw Phumraphi Boonthai Narumol Chumuang Department of Information Technology Management King Mongkut's University of Technology North Bangkok Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Department Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand

This research study presents an algorithmic approach for the detection and identification of key components on license plates, including numerical digits, province codes, vehicle colors, and vehicle makes and types. The methodology integrates image processing techniques and deep learning methodologies to achieve comprehensive image analysis. The algorithm incorporates image preprocessing techniques to optimize the input images, followed by a deep neural network trained on a large annotated dataset. The algorithm utilizes image segmentation and character recognition techniques to detect and classify numerical digits on license plates. Similarly, it employs segmentation and classification processes for identifying province codes, vehicle colors, makes, and types. Extensive training ensures the algorithm's accuracy and robustness under varying conditions. The proposed algorithm demonstrates the potential for accurate license plate component detection and identification, with applications in law enforcement and traffic monitoring systems.

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Predicting User Preferences with XGBoost Learning to Rank Method 3

Predicting User Preferences with XGBoost Learning to Rank Me...

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3rd International Seminar on Research of information Technology and Intelligent Systems, ISRITI 2020

作者： Qomariyah, Nunung Nurul Kazakov, Dimitar Fajar, Ahmad Nurul Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta11480 Indonesia University of York Computer Science Department York United Kingdom Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781728184067

Learning user preferences become very important as the personalization systems grow rapidly in this current era. Offering special and personal services can be an added value for the companies to maintain their customer loyalty. Building a personalized recommendation requires a good machine learning model to understand the individual preferences. Every user can be presented with a list of items sorted by its score learned from the individual preferences. So the first couple items shown will be the most liked items by the user. We can borrow the Learning to Rank algorithm from information Retrieval to solve this problem. In this paper, we present the implementation of user preferences learning by using XGBoost Learning to Rank method in movie domain. We show the evaluation of three different approaches in Learning to Rank according to their Normalized Discounted Cumulative Gain (NDCG) score. We can conclude that in our case study, the pairwise approach appears to be the best solution to produce a personalized list of recommendation. © 2020 IEEE.

关键词： Recommender systems

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User Preference Modelling from Movie Database 7

User Preference Modelling from Movie Database

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Qomariyah, Nunung Nurul Kazakov, Dimitar Fajar, Ahmad Nurul Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta11480 Indonesia University of York Computer Science Department York United Kingdom Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Film industry becomes one of the largest amongst the other economic sectors worldwide nowadays. This industry also has a significant impact in the global economic market. Many movies have been produced each year. However, some movies still fail to reach break-even points every year. Players in this industry need to think carefully when spending most of their money to make a great and successful movie in the market. Several attempts also need to be made to estimate the success performance before a movie can be released. On the audience side, there is an indicator that we can use to decide that a movie is good by looking to its ratings. The industry can use this rating systems as a factor to be considered when evaluating the past movie performances to make better movie for the next production. This study aims to predict the movie ratings and find the most important features which correlate to the ratings. We performed an experiment on IMDb dataset which contains 5,043 movie metadata on four models of machine learning, Linear Regression (LR), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN). From the experiment, Random Forest achieves the best performance amongst the four models. © 2020 IEEE.

关键词： Decision trees

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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Learning Pairwise Preferences from Movie Ratings 7

Learning Pairwise Preferences from Movie Ratings

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Qomariyah, Nunung Nurul Fajar, Ahmad Nurul Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Preference Elicitation is now considered a crucial stage in recommender system. That is the stage where we collect and query preferences of the users as part of interactive decision support system. Preference elicitation can be performed using many ways, such as asking the user explicitly to give ratings or feedbacks for some products, or implicitly observing their behaviour. Once this preference has been collected by the system, it can then be used to generate item recommendation. Using explicit feedback itself, such as rating only, has several drawbacks. Using ratings is very subjective. Two users with similar taste might give different rating to the same item. A user also may not be consistent in giving number to express their liking. In other case, a user may like an item more than the other items, but feels difficult to give lower rating on the other item, because it means that the particular item is just as bad as the one he/she does not like. By showing two different items as a pair, it will be easier for the users to pick which one is the best. In this paper, we show the result of our experiment in predicting user pairwise preferences by using their movie ratings data. We evaluated five different machine learning algorithms to predict the data in pairwise preference format. The result shows that K-Nearest Neighbour and Random Forest outperformed the other algorithms. © 2020 IEEE.

关键词： Recommender systems

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Real-Time Credit Card Fraud Detection Surveillance System

Real-Time Credit Card Fraud Detection Surveillance System

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Cybernetics and Innovations (ICCI), International Conference on

作者： Pattarawan Thongthawonsuwan Thittaporn Ganokratanaa Patiyuth Pramkeaw Narumol Chumuang Mahasak Ketcham Department of Information Technology Management King Mongkut's University of Technology North Bangkok Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Department of Digital Media Technology Muban Chombueng Rajabhat University (MCRU) Thailand

In this research, the system that detects administrative behavior, credit card and developing corruption from credit cards through LINE applications. The objective of this research 1) To develop a risk notification system or suspect to be corruption of credit cards through the connection channels of LINE application 2) to measure the accuracy of the system developed. As for the notification to prevent the risk of credit card corruption The research methods are divided into 5 steps, namely Step 1, Step System analysis. Is a study and analysis to determine the needs of steps 2 steps, system design Is the process of designing tools used in research. Step 3 System development Is the process of developing the tools used in research. Step 4, the process of testing and correcting the system. Is the process of testing the tools used in research. Step 5 Summary Discuss results and suggestions. The research results are summarized as follows The results of the measurement and completeness of the data are very good as 86.67 percent. The results of the measurement of the correct conditions are as good as 80 percent and the performance measurement of time is in The criteria is as good as 86.67 percent. In conclusion, the corruption system from credit cards through the application alert. LINE can be used appropriately.

关键词： Technological innovation Surveillance Credit cards Time measurement Real-time systems Fraud Behavioral sciences

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Customer Service Support System by ITIL version 3 Operating Framework with the Geographic information System

Customer Service Support System by ITIL version 3 Operating ...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Tawan Thanamatee Thittaporn Ganokratanaa Patiyuth Pramkeaw Narumol Chumuang Mahasak Ketcham Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Department of Digital Media Technology Muban Chombueng Rajabhat University (MCRU) Thailand Department of Information Technology Management King Mongkut's University of Technology North Bangkok Bangkok Thailand

The purpose of this research was to develop a power consumer service support system, a case study of the Provincial Electricity Authority, Chumphon Province. By applying the ITIL version 3 operating framework to be applied in conjunction with the geographic information system, the efficiency of the application of the geographic information system was assessed, the quality of the system was assessed by experts and the satisfaction of general users was assessed. This system was developed to increase the efficiency and accuracy of electricity consumer services. receive follow-up notifications request for service and service complaints Able to display statistical reports in geographic information formats resulting in standardized customer service management. Evaluation results of the efficiency of the system in finding and displaying the coordinates of service locations have an average overall efficiency of F-Measure of 92 percent and the quality assessment of experts at a good level with an average of 4.17 deviations. The standard was 0.51 and the satisfaction assessment results of general users were at a good level with an average of 4.35 and a standard deviation of 0.42 from both groups of assessors. It can be concluded that the developed system can be applied to real work efficiently and effectively

关键词： Heart Technological innovation Customer services Standards organizations Government Quality assessment information technology

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Smart Alarm Clock for Effective Sleep Health

Smart Alarm Clock for Effective Sleep Health

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Cybernetics and Innovations (ICCI), International Conference on

作者： Kantapat Kwansomkid Mahasak Ketcham Thittaporn Ganokratanaa Patiyuth Pramkeaw Narumol Chumuang Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Department of Information Technology Management King Mongkut's University of Technology North Bangkok Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Department of Digital Media Technology Muban Chombueng Rajabhat University (MCRU) Thailand

The purpose of this research is to study and develop an alarm clock to help wake up at the set time. Without having to snooze the alarm and not be so sleepy that you have to go back to sleep again. to help reduce the risk of sleep-related disease By using various sensors, the Internet Of Thing (IoT) technology is used for health benefits. And from the evaluation of the accuracy of the system by experts. has a total value of 86%

关键词： Technological innovation Turning Sensor systems Mobile applications Internet of Things Intelligent sensors Cybernetics

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