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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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Survival analysis of Kasturi tobacco plants in Jember district using the stratified cox and extended cox models.

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MethodsX 2025年 14卷

作者： Pratama, Angga Iryanto Rantini, Dwi Ningrum, Ratih Ardiati Ramadan, Arip Othman, Fazidah Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Research Group of Data-Driven Decision Support System Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaya Malaysia

In 2022, tobacco was the top export commodity, generating $106.3 million in sales. East Java Province contributes significantly to Indonesia's tobacco production, with an annual output of 188.6 thousand tons. Jember Regency is the area in East Java Province which is a Kasturi tobacco cultivation area. The productivity of Kasturi tobacco continues to decrease due to several factors that can affect the survival of the tobacco. This study aims to analyze the survival of Kasturi tobacco by creating stratified Cox and extended Cox models in order to handle factors that cannot fulfill the proportional hazard assumption. Based on the results of the analysis, the stratified Cox model is the best model with AIC and BIC values of 798.108 and 805.5748 respectively, while the extended Cox has AIC and BIC values of 840.2186 and 850.1732 respectively. Variables that can significantly affect the survival of Kasturi tobacco are the variable concentration of ZA fertilizer, pesticide concentration. The addition of ZA fertilizer must be appropriate, because if excessive it can cause poisoning of tobacco plants. Likewise with pesticides, if excessive it will cause damage to the leaves. This policy can increase the productivity of Kasturi tobacco. Then, Jember Regency contributes greater export capacity. • This paper aims to determine the factors that affect the survival of Kasturi tobacco plants in Jember • By using the extended Cox model, the best one is obtained using the Heaviside function with AIC and BIC values of 840.2186 and 850.1732, respectively • The stratified Cox model is better than the extended Cox model, which has AIC and BIC values of 798.108 and 805.5748, respectively © 2025 The Author(s)

关键词： Cox proportional hazard Extended cox Kasturi tobacco Productive agriculture Stratified cox

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Reinforcement Learning-Based Edge Caching for IoT in Compute-First Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Asmat, Hamid Din, Ikram Ud Almogren, Ahmad Rodrigues, Joel J. P. C. Department of Information Technology University of Haripur Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Chair of Cyber Security Department of Computer Science Riyadh11633 Saudi Arabia Post-graduation Program in Electrical Engineering Teresina Brazil

6G networks require ultra-low latency and intelligent resource management in IoT environments. Compute-First Networking (CFN), integrating distributed computing and edge caching, addresses backhaul congestion. This paper proposes CaRL, a DRL-based caching framework using TD3 algorithm. CaRL optimizes cache placement at edge nodes by incorporating real-time network state, content popularity, and resource utilization. Simulations show CaRL improves cache hit ratio, reduces latency, and enhances bandwidth efficiency, and ensures secure caching decisions under dynamic network conditions. Unlike existing methods, CaRL adapts to dynamic network conditions by integrating latency, CPU, and memory metrics, demonstrating scalability and adaptability in CFN-driven 6G environments. © 1975-2011 IEEE.

关键词： Edge computing

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Agenda of Solutions to Mitigate the Challenge of Polarization of Extreme Positions in Social Media Environments

Agenda of Solutions to Mitigate the Challenge of Polarizatio...

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International Conference on computer Supported Cooperative Work in Design

作者： Ana Paula Pimentel Claudia Motta António Correia Daniel Schneider Postgraduate Program in Informatics (PPGI) UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research (NCE) UFRJ Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal College of Information Science & Technology University of Nebraska at Omaha USA

This paper proposes an agenda of possible solutions relevant to the challenge of polarization of extreme positions. The challenge of mitigating polarization of extreme positions has been a hot topic in politics, journalism, new media, and, more recently, in CSCW and HCI research. In this study, the main focus is to investigate the harmful effects of polarization from a neuroscience point of view. The suggested solutions are based on theories of social cognitive neuroscience and encourage awareness of inappropriate behavior, an increase in the diversity of arguments, and the exercise of individual and collective responsibility, to mitigate the problem of polarization that threatens public debate and, consequently, democracy.

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Classification Of Aedes Mosquito Larva Using Convolutional Neural Networks And Extreme Learning Machine

Classification Of Aedes Mosquito Larva Using Convolutional N...

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International Conference on Electrical, Telecommunication and computer Engineering (ELTICOM)

作者： Pauzi Ibrahim Nainggolan Syahril Efendi Mohammad Andri Budiman Maya Silvi Lydia Ivan Joshua Dhani Syahputra Bukit Romi Fadillah Rahmat Ledy Afrida Sinaga Edi Subroto Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Information Technology Universitas Sumatera Utara Medan Indonesia Department of Public Health Universitas Sumatera Utara Medan Indonesia Environmental health and diseases control Ministry of Health Medan Indonesia Department of midwifery study program STIKes Mitra Husada Medan Sumatera Utara Indonesia

The spread of diseases transmitted by Aedes mosquitoes, such as Dengue fever, Chikungunya (CHIKV), and Zika, poses a severe health problem in many countries. Aedes mosquitoes can be found in tropical and subtropical areas. One method to control the diseases is by controlling the population of Aedes mosquitoes, and one way to control the population of Aedes mosquito is by exterminating Aedes mosquito larvae. Identifying mosquito larvae, however, demands a considerable amount of effort, time, and specialized knowledge about the morphology and anatomy of mosquito larvae to accurately classify the various types. Therefore, a system is needed to classify Aedes and non-Aedes mosquito larvae. In this study, classification was carried out using larvae images taken using a digital microscope. The methods used to classify mosquito larvae images are Convolutional Neural Networks (CNN) to extract features from the larvae image and Extreme Learning Machine (ELM) to classify the larvae image. The result obtained from evaluating the CNN-ELM model is an accuracy of 98%, f1-score of 99% for Aedes larvae, and fl-score of 96% for non-Aedes larvae.

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Optimization of hyper parameter bandwidth on naïve Bayes kernel density estimation for the breast cancer classification 2

Optimization of hyper parameter bandwidth on naïve Bayes ke...

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2nd International Conference on information and Communications technology, ICOIACT 2019

作者： Sasongko, Theopilus Bayu Arifin, Oki Al Fatta, Hanif Information Technology Department Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Management of Informatics Study Program Economy and Business Departement Politeknik Negeri Lampung Lampung Indonesia

ISBN: (纸本)9781728116556

Cancer is viewed as the most harmful disease in the world. One of the most deadly cancer diseases attacking the women is breast cancer. In this paper, used Naïve Bayes classifier Kernel Density Estimation in breast cancer Wisconsin dataset take from UCI data repository. Naïve Bayes with Kernel Density Estimation was used as it could enable the Naïve Bayes to process the quantitative data. However, the performance of KDE (kernel density estimation) highly depends upon the size of the bandwidth parameter used to control the curve. The problem an accurate choice of parameter bandwidth (h) using trial and error technique requires a long process. A large bandwidth (h) will over-smooth the density and mask the structure in the data, a small bandwidth (h) will yield a density estimate that is spiky and very hard to interpret. The aim of this research is to compare the optimization of grid search parameter with the genetic algorithm to determine the bandwidth parameter (h) on the Naïve Bayes Kernel Density Estimator algorithm. The parameters used to compare the two optimization methods included accuracy, AUC (Area under Curve), and computation time. The experiment results on the test of the significance value on the comparison of grid search and algorithm in determination of the bandwidth parameter (h) naïve Bayes kernel density used Mann-Whitney Test. The result of the significance ranking showed that mean rank of the genetic algorithm accuracy was 489.95 better than the mean value of the rank of grid search accuracy at only 189.05, mean rank of AUC genetic algorithm was 491.82 better than mean rank of AUC grid search only at 187.18. The mean rank of computation time grid search was 243.37 faster than mean rank of computation time genetic algorithm at 435.63. © 2019 IEEE

关键词： Classification (of information)

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Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization

arXiv

引用

arXiv 2022年

作者： Wang, Shengbo Wen, Shiping Yang, Yin Cao, Yuting Shi, Kaibo Huang, Tingwen The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia College of Science and Engineering Hamad Bin Khalifa University Doha5855 Qatar School of Information Science and Engineering Chengdu University Chengdu611040 China Science Program Texas A & M University at Qatar Doha23874 Qatar

This paper presents an innovative efficient safety-critical control scheme for nonlinear systems by combining techniques of control barrier function (CBF) and online time-varying optimization. The idea lies in that when directly calling the complete optimization solvers used in the CBF method, such as CBF-based quadratic programming (CBF-QP), is computationally inefficient for complex tasks, the suboptimal solutions obtained from online learning techniques can be an alternatively reliable choice for ensuring both efficiency and control performance. By using the barrier-based interior point method, the original optimization problem with CBF constraints is reduced to an unconstrained one with approximate optimality. Then, Newton- and gradient-based continuous dynamics are introduced to generate alternative cheap solutions while ensuring safety. By further considering the lag effect of online tracking, a prediction term is added to the dynamics. In this way, the online cheap solutions are proven to exponentially converge to the time-varying suboptimal solutions of the interior point method. Furthermore, the safety criteria are established, and the robustness of the designed algorithms is analyzed theoretically. Finally, the effectiveness is illustrated by conducting two experiments on obstacle avoidance and anti-swing tasks. © 2022, CC BY-NC-ND.

关键词： Quadratic programming

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Sodium-ion battery cycle life prediction using machine learning

Sodium-ion battery cycle life prediction using machine learn...

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International Conference on Green Energy, Computing and Intelligent technology 2024 (GEn-CITy 2024)

作者： Linfeng Liu Filbert H. Juwono W. K. Wong Erick Purwanto Tingyan Jin Yuyang Zhen Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou People's Republic of China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing People's Republic of China School of Instrument Science and Technology Beijing University of Technology Beijing People's Republic of China

ISBN: (数字)9781837242863

In this study, we explore the classification and prediction capabilities of three models—Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)—on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction.

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Linguistic structure analysis of Thai and white hmong languages for machine translation 4

Linguistic structure analysis of Thai and white hmong langua...

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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： Prasomsuk, Sukchatri Phumsaranakhom, Phuwitson Nuankaew, Pratya Nuankaew, Wongpanya Information Technology Program University of Phayao Phayao Thailand Computer Engineering Program University of Phayao Phayao Thailand Business Computer Program University of Phayao Phayao Thailand Information Technology Program Rajabhat Mahasarakham University Thailand

ISBN: (纸本)9781538680728

The Machine Translation (MT) is an automatic human language translation from one language to another language by computer namely 'Interlingual MT'. The most importance key of translation process is to understand the structure of the language. The research objectives are study the appropriate language structure, present the translation pattern, and develop a dictionary for Thai and White Hmong translations. The scope of the research is limited to the language structure which defines the word not exceed than 4 syllables. Linguistic structure analysis used the translation pattern and Human Aided Machine Translation (HAMT) process. The experimental results indicate that the translation process can be used in two ways are translation from primary language to target language and direct translation. Our dictionary has been developed with classification 6,677 words,3,229 sentences were employed for system testing. The proposed prototype translation system is achieved a superior efficiency outcome of translation than Google and Microsoft by F-Measure are 0.496, 0.316, and 0.244 respectively. For future work, the researcher intend to present of the appropriate machine translation algorithm and committed to developing a translation application between Thai and Hmong languages. © 2019 IEEE.

关键词： Machine translation

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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