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Informatica (Slovenia) 2025年第19期49卷 183-196页

作者： Hakim, Dimara Kusuma Gernowo, Rahmat Nirwansyah, Anang Widhi Haryanto, Totok Department of Informatics Universitas Muhammadiyah Purwokerto 53182 Indonesia Department of Physic Faculty of Mathematics and Natural Sciences Diponegoro University Indonesia Department of Geography Education Universitas Muhammadiyah Purwokerto 53182 Indonesia Bachelor of Management Program Faculty of Economics and Business Universitas Muhammadiyah Purwokerto 53182 Indonesia

Conventional flood hazard maps depict a static perspective on flood risk and are helpful in flood forecasting. Then, time-series flood forecasting research was developed and widely conducted. Still, it has limitations because the univariate method used does not consider the influence of other variables, unlike regression. Also, the determination of flood hazard weights, which is usually conducted through empirical studies using AHP, has a good level of accuracy but has the drawback of expert subjectivity. A new approach is proposed for flood hazard forecasting, combining the Triple ES method with NN and determining weights in a data-driven manner using NN. A combination of time series and regression methods (significantly non-linear), namely Triple ES with NN, results in good accuracy with an MSE value of 3.03, MAE 1.20, RMSE 1.74, R2 0.54, and a MAPE of 32.93%. The evaluation results for flood hazard weight determination with an MSE of 0.0111 and a MAPE of 7.81% show promising results, and the weights can be used in Hazard Flood GIS. Visualization in the form of a GIS Hazard Map can be done after all related raster data have been combined. The computational outcomes, particularly the MSE and MAPE values, demonstrate the effectiveness of the proposed approach, providing a clear understanding of the model's performance. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Linear regression

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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Efficient Maximum Euclidean Distance Transform Computation in Component Trees Using the Differential Image Foresting Transform

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Journal of Mathematical Imaging and Vision 2025年第3期67卷 1-23页

作者： Silva, Dennis J. Miranda, Paulo A. V. Alves, Wonder A. L. Hashimoto, Ronaldo F. Kosinka, Jiří Roerdink, Jos B. T. M. Institute of Mathematics and Statistics University of São Paulo São Paulo Brazil Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen The Netherlands Informatics and Knowledge Management Graduate Program Nove de Julho University São Paulo Brazil

The distance transform is a crucial technique in binary image processing, assigning the distance to the nearest contour to each foreground pixel. In this extended version of our previous work, we enhance our method for computing the maximum distance transform (DT) value, now utilizing the optimized differential image foresting transform (DIFT) and improved contour extraction processes. These advancements enable more efficient computation of the maximum DT value across all connected components of a grayscale image, significantly reducing computational time by intelligently reusing DIFT trees rooted at contour points (DIFT seeds). Our optimized algorithm now achieves processing speeds that are twice as fast as our previous differential method. The proposed attribute, maximum distance, which measures the thickness of objects within the image, has proven pivotal in different image processing approaches. We showcase this through detailed illustrations of attribute opening, extinction value filters, watershed, and ultimate attribute openings.

关键词： Binary images

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Principal Curvatures Estimation with Applications to Single Cell Data

Principal Curvatures Estimation with Applications to Single ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yanlei Mezrag, Lydia Sun, Xingzhi Xu, Charles Macdonald, Kincaid Bhaskar, Dhananjay Krishnaswamy, Smita Wolf, Guy Rieck, Bastian Université de Montréal Dept. of Math. & Stat Canada Mila - Quebec AI Institute MontréalQC Canada Yale University Dept. of Comp. Sci. United States Dept. of Genetics United States Applied Mathematics Program New HavenCT United States Université de Fribourg Department of Informatics FR Fribourg Switzerland Helmholtz Zentrum München Institute of AI for Health BY Munich Germany

ISBN: (纸本)9798350368741

The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learning consists in hypothesizing that datasets lie on a lower dimensional manifold. This allows to study the geometry of point clouds by extracting meaningful descriptors like curvature. In this work, we will present Adaptive Local PCA (AdaL-PCA), a data-driven method for accurately estimating various notions of intrinsic curvature on data manifolds, in particular principal curvatures for surfaces. The model relies on local PCA to estimate the tangent spaces. The evaluation of AdaL-PCA on sampled surfaces shows state-of-the-art results. Combined with a PHATE embedding, the model applied to single-cell RNA sequencing data allows us to identify key variations in the cellular differentiation. © 2025 IEEE.

关键词： Gaussian curvature Principal curvature principal directions single-cell

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Principal Curvatures Estimation with Applications to Single Cell Data

Principal Curvatures Estimation with Applications to Single ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yanlei Zhang Lydia Mezrag Xingzhi Sun Charles Xu Kincaid Macdonald Dhananjay Bhaskar Smita Krishnaswamy Guy Wolf Bastian Rieck Dept. of Math. & Stat. Université de Montréal Mila – Quebec AI Institute Montréal CA Dept. of Comp. Sci. Yale University Dept. of Genetics Applied Mathematics Program New Haven CT USA Helmholtz Zentrum München Institute of AI for Health Munich BY DE Department of Informatics Université de Fribourg Fribourg CH

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Manifolds Sequential analysis Adaptation models Data analysis RNA Transcriptomics Curve fitting Speech processing Tuning Principal component analysis

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TWO ALGORITHMS FOR LC^1 UNCONSTRAINED OPTIMIZATION

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Journal of Computational mathematics 2000年第6期18卷 621-632页

作者： Wen-yu Sun R.J.B.de Sampaio Jin-Yun Yuan School of Mathematics and Computer Science Nanjing Normal UniversityNanjing 210097China Applied Informatics Program Pontifical Catholic University of ParanaCuritibaPR81611-970Brazil Department of Mathematics Federal University of ParanaCuritibaPR81531-990Brazil

Presents two algorithms for LC unconstrained optimization problems which use the second order Dini upper directional derivative. Simplicity of the methods to use and perform; Discussion of related properties of the it... 详细信息

关键词： nonsmooth optimization directional derivative Newton-like method convergence trust region method

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"CoVid" Analysis using Online-Based Problem-Solving Approach on informatics mathematics Subject 4

"CoVid" Analysis using Online-Based Problem-Solving Approach...

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4th International Conference on Statistics, mathematics, Teaching, and Research, ICSMTR 2021

作者： Ashari, N.W. Salwah Ma-Rufi Informatics Study Program Universitas Cokroaminoto Palopo Indonesia Mathematics Education Study Program Universitas Cokroaminoto Palopo Indonesia

During the COVID-19 pandemic, lecturers carry out online learning and this condition also force student to learn independently. One alternative solution given is online-based problem-solving approach. This approach are suitable implemented in the mathematics informatics course. The Approach challenges students to be able to express every result of thinking, transform information into various forms of representation, as well as the ability to adapt to online learning during this pandemic. This research is case study research that aims to analyse the Communication Skills, Visual thinking, and Adaptability (COVid) of students in the mathematics informatics course through Online-Based Problem-Solving Approach. This course examines combinatorics and pigeon holes principle. This lesson is useful in the context of making software that requires certain calculations and logic. The subjects of this study were second semester students of the 2020/2021 academic year at the informatics Study program at Univeritas Cokroaminoto Palopo. Data was collected by using assignments, questionnaires, and interviews to obtain valid data. The results show that only a few students are able to express every result of thinking, transform information into various forms of representation, as well as the ability to adapt to online learning during this pandemic. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Students

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Principal Curvatures Estimation with Applications to Single Cell Data

arXiv

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arXiv 2025年

作者： Zhang, Yanlei Mezrag, Lydia Sun, Xingzhi Xu, Charles Macdonald, Kincaid Bhaskar, Dhananjay Krishnaswamy, Smita Wolf, Guy Rieck, Bastian Université de Montréal Dept. of Math. & Stat. QC Montréal Canada Mila – Quebec AI Institute MontréalQC Canada Yale University Dept. of Comp. Sci. New HavenCT United States Dept. of Genetics New HavenCT United States Applied Mathematics Program New HavenCT United States Université de Fribourg Department of Informatics Fribourg Switzerland Helmholtz Zentrum München Institute of AI for Health Munich Germany

关键词： Spatio-temporal data

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Applying social network analysis on courses relationship in informatics mathematics curriculum 19

Applying social network analysis on courses relationship in ...

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4th International Conference on Information and Education Innovations, ICIEI 2019

作者： Bussaban, Kanyarat KhantaVchai, Asekha Informatics Mathematics Program Suan Sunandha Rajabhat University 1 Uthong Nork Rd Dusit Bangkok Thailand

ISBN: (纸本)9781450371698

This study investigates the relationship the courses achievement of undergraduate students in informatics mathematics curriculum. Social Network Analysis and Pearson product Moment Correlation Coefficient are used to determine which courses are significant important and influence predictors. Data collected is from the sample of 91 online report submitted by science graduated students who have graduated during the academic year 2010-2017. The results of the study indicate that Linear Algebra and Discrete mathematics are the highest score of weighted degree centrality as mathematics information technology is the highest score of betweenness centrality and English for informatics mathematics influence over the whole courses. © 2019 Association for Computing Machinery.

关键词： Curricula

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Optimizing Advantage Actor-Critic with Policy Gradient and Deep Q-learning to Maximize Profit in Forex Trading Prediction 1

Optimizing Advantage Actor-Critic with Policy Gradient and D...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Baradja, Abdillah Gernowo, Rahmat Wibowo, Adi Universitas Diponegoro Doktoral Program of Information System Semarang Indonesia Universitas Diponegoro Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350306156

Financial markets, such as forex markets, are inherently complex, with high volatility, noise, trends, and market shocks. Therefore, designing effective trading strategies is crucial for maximizing investment returns. The implementation of deep reinforcement learning (DRL) in financial trading has become an attractive research topic in recent years. This study proposes Advantage Actor-Critic-based algorithms (A2C) algorithms to learn forex trading strategies using deep reinforcement schemes. The ensemble trading strategy was obtained by combining the best features of the two algorithms' Policy Gradient as Actor and Q-learning as Critic, resulting in a robust strategy that adapts to different market situations. Our experiment on the EURUSD currency on the 4 hour candlestick timeframe using five years of data history was divided into three parts for training, validating, and testing. The A2C agent learns to buy, sell, and hold a trade with the goals of maximizing profits and reducing losses. Our proposed method provides an effective and robust solution to various financial trading problems using deep reinforcement learning. Our proposed methods provide effective and robust solutions to various financial trading problems using deep reinforcement learning. The evaluation results improved from each training, where the Cumulative Return evaluation increased from 29.65 to 112.74, the Maximum Drawdown reduced from 36.18% to 4.48%, the Sharp Ratio increased from 0.94 to 2.47%, and the AHPR increased from 0.19% to 3.56%. This indicates that longer training data result in higher cumulative rewards and an increase in total profit. © 2023 IEEE.

关键词： Reinforcement learning

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