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2021 International Seminar on Application for Technology of Information and Communication, iSemantic 2021

作者： Hariyadi, Guruh Taufan Purwanto Isnanto, R Rizal Wibowo, Adi Warsito, Budi Universitas Diponegoro Doctoral Program of Information Systems Semarang Indonesia Universitas Diponegoro Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Universitas Diponegoro Faculty of Engineerig Department of Computer Engineering Semarang Indonesia Universitas Diponegoro Faculty of Science and Mathematics Departement of Informatics Semarang Indonesia

ISBN: (纸本)9781665428040

Education to the public on green products has been widely carried out. Consumers who buy green products can be profiled by segmenting green consumers. In green marketing, consumer education on sustainable consumption is increasingly needed in recent years due to the problem of environmental pollution which poses a serious threat to the world population. Concern for the environment is an important topic in most countries, and individuals also study environmental issues. Individuals or groups of individuals who are 'ecologically aware' are also referred to as 'green consumers.' Such consumers are considered to have more concern for the environment. This study examined the predictions on the effect of the Green Perceived Value variables consisting of functional, conditional, and social values on the purchase intention of green consumers. It also tested the comparisons with other classification models. The models used in this research were Random Forest, Decision Tree, and Neural Network.. © 2021 IEEE.

关键词： Consumer behavior

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An Efficient Algorithm to Update Incremental Attributes in Morphological Trees

SSRN

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SSRN 2022年

作者： Gobber, Charles Ferreira Hashimoto, Ronaldo Fumio Alves, Wonder Alexandre Luz Informatics and Knowledge Management Graduate Program Universidade Nove de Julho São Paulo Brazil Department of Computer Science Institute of Mathematics and Statistics Universidade de São Paulo São Paulo Brazil

Attribute Filters are powerful image simplification operators that have very good contour-preservation properties. They can be efficiently computed using morphological trees. The most common strategy to compute attribute filters using morphological trees is based on three steps: (i) tree construction: the tree is built along with its attributes;(ii) filtering: we simplify the tree by removing some of its nodes based on some attribute and a filtering rule defined on a threshold value;and (iii) image reconstruction: this is the final phase where the tree is converted back to an image leading to an attribute filter. However, after filtering a few attributes in tree may change and they must be updated, since some attribute filters are computed by filtering the simplified tree again, but with a different threshold. In this paper it is presented an efficient algorithm to update incremental attributes in morphological trees with low memory consumption and fast computation. © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Minimum premium epidemic model S1S2EIR acute respiratory infectious disease impact of haze forest and land fires

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AIP Conference Proceedings 2024年第1期3083卷

作者： Haposan Sirait Sukono Sukono Nursanti Anggriani Hafizan Bin Juahir Doctor Program of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Sumedang Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Sumedang Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia

Acute respiratory infection (ARI) is the most critical disease in forest fire prone areas. The occurrence of forest fires results in a significant increase in infected cases and an increase in the public health burden. Community tries to cover future medical costs due to the ARI outbreak by paying premiums to insurance as a financial burden to anticipate future expenses. The actuarial profession has encouraged actuaries to determine premiums, because when the premiums are relatively high, there is a high probability that healthy policyholders may not choose this option. On the other hand, when the premium is relatively low, the insurer may not be able to collect enough premiums to create a negative reserve, of course, this is not desirable. This study aims to determine the minimum premium based on the dynamics of ARI disease transmission expressed using the S1S2EIR compartment model. We consider the fractional of each compartment as a probability. The minimum premium obtained is expected to guarantee reserves so that it is sufficient to make future claim payments.

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Identification of finite circular metric spaces by magnitude and Riesz energy

arXiv

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arXiv 2024年

作者： Kodama, Hiroki O’Hara, Jun Center for Mathematical Engineering Musashino University 3-3-3 Ariake Koto-ku Tokyo135-8181 Japan Interdisciplinary Theoretical and Mathematical Sciences Program RIKEN 2-1 Hirosawa Saitama Wako-shi351-0198 Japan Department of Mathematics and Informatics Faculty of Science Chiba University 1-33 Yayoi-cho Inage Chiba263-8522 Japan

We study the problem whether regular polygons and cycle graphs can be identified by the magnitude or the discrete Riesz energy. We give an affirmative answer for a finite number of cases when the number of vertices is small and a negative answer for an infinite number of wide cases by giving a way to construct non-isometric spaces, which we call isomers, with the same magnitude or the discrete Riesz energy. We study whether isomers can be distinguished by the magnitude homology *** Codes 51F99 Copyright © 2024, The Authors. All rights reserved.

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2D Basement Relief Inversion using Sparse Regularization.

arXiv

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arXiv 2024年

作者： Barboza, Francisco Márcio da Cunha Romão Silva, E. Arthur Anthony de Carvalho, Bruno Motta Department of Computing and Technology Federal University of Rio Grande do Norte Rio Grande do Norte Caicó Brazil Graduate Program on Systems and Computing Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil

Basement relief gravimetry is a key application in geophysics, particularly important for oil exploration and mineral prospecting. It involves solving an inverse geophysical problem, where the parameters of a geological model are inferred from observed data. In this context, the geological model consists of the depths of constant-density prisms representing the basement relief, and the data correspond to the gravitational anomalies caused by these prisms. Inverse geophysical problems are typically ill-posed, as defined by Hadamard, meaning that small perturbations in the data can result in large variations in the solutions. To address this instability, regularization techniques, such as those proposed by Tikhonov, are employed to stabilize the solutions. This study presents a comparative analysis of various regularization techniques applied to the gravimetric inversion problem, including Smoothness Constraints, Total Variation, the Discrete Cosine Transform (DCT), and the Discrete Wavelet Transform (DWT) using Daubechies D4 wavelets. Optimization methods are commonly used in inverse geophysical problems because of their ability to find optimal parameters that minimize the objective function—in this case, the depths of the prisms that best explain the observed gravitational anomalies. The Genetic Algorithm (GA) was selected as the optimization technique. GA is based on Darwinian evolutionary theory, specifically the principle of natural selection, where the fittest individuals in a population are selected to pass on their traits. In optimization, this translates to selecting solutions that most effectively minimize the objective function. The results, evaluated using fit metrics and cumulative error analysis, demonstrate the effectiveness of all the regularization techniques and the Genetic Algorithm. Among the methods tested, the Smoothness constraint was briefly the most effective for the first and second synthetic models. For the third model, which was based on re

关键词： Discrete wavelet transforms

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Implementation of Integrated Bayes Formula and Support Vector Machine for Analysing Airline's Passengers Review 5

Implementation of Integrated Bayes Formula and Support Vecto...

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5th International Conference on Energy, Environmental and Information System, ICENIS 2020

作者： Tegar Satria, Aditya Mustafid Mutiara Kusumo Nugraheni, Dinar Master Program of Information System School of Postgraduate Studies Diponegoro University Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Nowadays, the utilization of Internet of Things (IoT) is commonly used in the tourism industry, including aviation, where passengers of flight services can rate their satisfaction levels towards the product and service they use by writing their reviews in the form of text-based data on many popular websites. These passenger reviews are collections of potential big data and can be analyzed in order to extract meaningful informations. Some text mining algorithms are already in common use, including the Bayes formula and Support Vector Machine methods. This research proposes an implementation of the Bayes and SVM methods where these algorithms will operate independently yet integrated with other modules such as input data, text pre-processing and shows output result concisely in one single information system. The proposed system was successfully delivered 1000 documents of passenger reviews as input data, then after implemented the pre-processing method, the Bayes formula was used to classify the document reviews into 5 categories, including plane condition, flight comfort, staff service, food and entertainment, and price. While simultanously, the positive and negative sentiment contained in the review document was analyzed with SVM method and shows the accuracy score of 83.6% for a training to testing set ratio of 50:50, while 82.75% accuracy for the 60:40 ratio, and 83.3% accuracy for the 70:30 ratio. This research shows that two different text mining algorithms can be implemented simultaneously in a effective and efficient way, while still providing an accurate and satisfying performance results in one integrated information system. © The Authors, published by EDP Sciences, 2020.

关键词： Support vector machines

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What we should learn from pandemic publishing

arXiv

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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Information System for Evaluating Specific Interventions of Stunting Case Using K-means Clustering 5

Information System for Evaluating Specific Interventions of ...

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5th International Conference on Energy, Environmental and Information System, ICENIS 2020

作者： Sartika, Widya Suryono, Suryono Wibowo, Adi Magister Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

One of the causes of death of children under five is chronic malnutrition or stunting. The government has made a policy as an effort to reduce stunting. Specific intervention nutrition programs are activities that directly address the occurrence of stunting. Evaluation of edit interventions that have currently been carried out but have not separated the process based on intervention indicators so that it results in a long processing time and large exit costs so we need a system that can accelerate the process of evaluating specific interventions. Information systems for evaluating specific interventions can be a solution to this problem. In this study, an information system for evaluating specific interventions of stunting cases was developed web-based to facilitate real-time operation. The evaluation information system is designed to collect national data and carry out a clustering process using the k-means method and evaluation stucture cluster using silhouette method. In this study an evaluation value of 34 provinces was obtained which was divided into several clusters as a result of evaluation process in the information system. © The Authors, published by EDP Sciences, 2020.

关键词： Information systems

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Human Activity Prediction using Long Short Term Memory 5

Human Activity Prediction using Long Short Term Memory

引用

5th International Conference on Energy, Environmental and Information System, ICENIS 2020

作者： Septiadi, Jaka Warsito, Budi Wibowo, Adi Master Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Early symptoms of dementia is one of the causes decrease in quality of life. Human activity recognition (HAR) system is proposed to recognize the daily routines which has an important role in detecting early symptoms of dementia. Long Short Term Memory (LSTM) is very useful for sequence analysis that can find the pattern of activities that carried out in daily routines. However, the LSTM model is slow to achieving convergence and take a long time during training. In this paper, we investigated the sequence of actions recorded in smart home sensors data using LSTM model, then the model will be optimized using several optimization methods. The optimization methods were Stochastic Gradient Descent (SGD), Adagrad, Adadelta, RMSProp, and Adam. The results showed that using Adam to optimized LSTM is better than other optimization methods. © The Authors, published by EDP Sciences, 2020.

关键词： Long short-term memory

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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

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.

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

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