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Toward Sustainable Development Goals (SDGs) with Statistical Modeling: Recursive Bivariate Binary Probit

IAENG International Journal of Applied mathematics 2024年第8期54卷 1515-1521页

作者： Ratnasari, Vita Utama, Syirrul Hadi Dani, Andrea Tri Rian Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia the Department of Statistics Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya Indonesia Statistics Study Program Department of Mathematics Faculty of Mathematics and Natural Sciences Mulawarman University Samarinda Indonesia

Poverty is still a global problem that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. In 2021, West Papua province will have the 2nd most significant percentage of poor people after Papua province, with 21.84% of the poor population. Poor households in West Papua province are dominated by families, with the Head of Household (KRT) working in the agricultural sector at 65.10 %. In this research, joint modelling was carried out between the level of household welfare and the employment sector of the head of the household in the West Papua province. It is suspected that these two variables have endogeneity problems, where one of the response variables becomes a predictor variable in the other equation, so a recursive bivariate binary probit regression model is used. Recursive bivariate binary probit regression parameter estimation uses Maximum Likelihood Estimation (MLE), but the results are not closed form, so it is continued using the Newton-Raphson iteration method. The results of hypothesis testing show that partially, variables that significantly influence the level of household welfare include the variable marital status, KRT formal/informal workers, health complaints, asset ownership status, migration status, number of household members, classification of area of residence (Village/City), age of head of household, and employment sector of head of household. Meanwhile, variables that significantly influence the choice of working in the agricultural sector include the director of household education, classification of area of residence (rural/city), and the age of the head of household. © (2024), (International Association of Engineers). All rights reserved.

关键词： Maximum likelihood estimation

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Statistical Modeling: A New Regression Curve Approximation using Mixed Estimators Truncated Spline and Epanechnikov Kernel

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Engineering Letters 2023年第4期31卷 1649-1655页

作者： Sifriyani Dani, Andrea Tri Rian Fauziyah, Meirinda Budiantara, I. Nyoman Statistics Study Program Department of Mathematics Faculty of Mathematics and Natural Sciences Mulawarman University Samarinda Indonesia Statistics Department Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

In the era of computation, researchers have paid significant attention to the nonparametric regression method. Nonparametric regression has the benefit of a high degree of modeling flexibility. Developing a mixed estimator truncated spline-Epanechnikov kernel is the most recent innovation in this study. The truncated spline estimator excels at handling data whose behavior varies at predetermined intervals. In contrast, the Epanechnikov kernel estimator has a more flexible structure and excels at modeling data that does not adhere to a particular pattern. Maximum Likelihood Estimation is utilized to estimate parameters. The concluding section of this study will discuss the estimator properties of the mixed estimators truncated spline and Epanechnikov kernel models. The proposed can be utilized for additional analysis in the field of nonparametric regression. © 2023, International Association of Engineers. All rights reserved.

关键词： Maximum likelihood estimation

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GeoBeeDashboard: An Intelligent Geospatial Platform for Enhancing Waste Management in Tangerang City 13

GeoBeeDashboard: An Intelligent Geospatial Platform for Enha...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Muljana, Richie Deffansyah, Syauqi Akmal Andrew, Hari Gunawan, Alexander Agung Santoso Irwansyah, Edy Pramudya, Fabian Surya Ario, Muhamad Keenan Syahputra, Muhammad Edo Achmad, Said School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368086

In this study, we created an Intelligent Geospatial Platform called GeoBeeDashboard. The GeoBeeDashboard combines advanced geospatial technologies and artificial intelligence (AI), allowing real-time data visualization and optimized decision-making across multiple sectors. The system architecture consists of a ***-based front end, a FastAPI-driven back end, and PostGIS for geospatial data management. One of the GeoBeeDashboard's primary use cases is addressing waste management issues in Tangerang City, Indonesia. As rapid urbanization increases waste generation, effective management becomes critical. The dashboard identifies critical trends and inefficiencies in Tangerang's districts by K-Means clustering waste management data from 2020 to 2023. Our findings identify distinct clusters, highlighting areas with effective waste management and those that require targeted interventions. The benefits that local government can get from GeoBeeDashboard by utilising insights based on clustering analysis of data per each district in Tangerang to optimise waste collection, allocate resources efficiently, and improve the policy-making process for each district in Tangerang. The GeoBeeDashboard improves local government capabilities by providing a scalable model for other cities facing similar urban challenges. In conclusion, GeoBeeDashboard provides local governments with a powerful tool for addressing waste management inefficiencies and fostering sustainable urban development. © 2024 IEEE.

关键词： Urban planning

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An Iterative Thresholding Method for the Minimum Compliance Problem

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Communications in Computational Physics 2023年第4期33卷 1189-1216页

作者： Luyu Cen Wei Hu Dong Wang Xiaoping Wang Department of Mathematics Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina The Program in Applied and Computational Mathematics Princeton UniversityPrincetonNew JerseyUSA School of Science and Engineering The Chinese University of Hong KongShenzhenGuangdong 518172China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big DataGuangdong 518172China

In this paper,we propose a simple energy decaying iterative thresholding algorithm to solve the two-phase minimum compliance *** material domain is implicitly represented by its characteristic function,and the problem is formulated into a minimization problem by the principle of minimum complementary *** prove that the energy is decreasing in each *** effective continuation schemes are proposed to avoid trapping into the local *** results on 2D isotropic linear material demonstrate the effectiveness of the proposed methods.

关键词： Minimum compliance convolution thresholding linear elasticity

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The planning strategy of robotics technology for nuclear decommissioning in Taiwan

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Nuclear Engineering and Technology 2024年第1期56卷 64-69页

作者： Tu, Chung Yi Chen, Kuen Tsann Ting, Kuen Yang Sheng, Chin Doctoral Program in Data Science and Industrial Analytics National Chung Hsing University Taichung County 402 Taiwan Department of Applied Mathematics National Chung Hsing University Taichung County 402 Taiwan Department of Mechanical Engineering Lunghwa Univesity of Science and Technology Taoyuan County 33306 Taiwan

According to the market research report, the nuclear decommissioning services market is currently experiencing considerable growth, with a projected Compound Annual Growth Rate (CAGR) of nearly 13% during the 2020–2024 forecast period. This expansion is primarily fueled by the advancement of Industry 4.0, in conjunction with the emergence of cutting-edge technologies such as the Internet of Things, big data, artificial intelligence, and 5G. Even though the fact that robots have already been utilized in the nuclear industry, their adoption has been hindered by conservative regulations. However, the nuclear decommissioning market presents an opportunity for the advancement of robotics technology. The British have already invested heavily in encouraging the use of intelligent robots for nuclear decommissioning, and other countries, such as Taiwan, should follow suit. Taiwan's flourishing robotics development industry in manufacturing, logistics, and other domains can be leveraged to introduce advanced robotics in the decommissioning of its nuclear power plants. By doing so, Taiwan can establish itself as a competitive player in the nuclear decommissioning services market for the next two decades. © 2023 Korean Nuclear Society

关键词： Intelligent robotics Nuclear decommissioning Planning strategy

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Pairwise Learning for Autism Spectrum Disorder Imbalanced Classification 6

Pairwise Learning for Autism Spectrum Disorder Imbalanced Cl...

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6th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Liu, Shu Wu, Qiang Yang, Xin Computational and Data Science PhD Program Middle Tennessee State University MurfreesboroTN United States University of Tennessee Department of Mathematics KnoxvilleTN United States Middle Tennessee State University Department of Computer Science MurfreesboroTN United States

ISBN: (纸本)9798350396133

This paper performs a classification task on data obtained from the Autism Brain Imaging data Exchange (ABIDE) repository. In real-world case analysis, the number of autism spectrum disorder (ASD) patients is much smaller than typically developed people. To address this issue, this paper proposes the utilization of pairwise robust support vector machine (PRSVM) algorithms to classify autism spectrum disorder (ASD) patients. In this project's experiments, the correlation matrix derived from functional magnetic resonance imaging (fMRI) data was employed as a classification feature. A comprehensive evaluation was conducted to compare the classification performance of PRSVM with various machine learning methods. The comparative analysis encompassed various aspects, including different data dimensions, imbalanced ratios, and sample sizes, providing valuable insights into the relative performance of the algorithms under different experimental conditions. The experimental results demonstrate that PRSVM can detect autistic patients more accurately when the data is imbalanced. Moreover, the results indicate that PRSVM outperforms or achieves comparable performance to other conventional classification methods in a variety of situations. Furthermore, our approach can be further improved by augmenting the training set with either exclusively normal person samples or by incorporating patient samples and normal people samples in a proportionate manner. This augmentation strategy holds promising application value, as it contributes to improving the performance and robustness of our method. © 2024 IEEE.

关键词： Diseases

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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PentaPen: Combining Penalized Models to Identify Important SNPs on Whole-genome Arabidopsis thaliana data 24

PentaPen: Combining Penalized Models to Identify Important S...

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16th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2024

作者： Kohli, Nikita Tomal, Jabed H. Lin, Wenjun Yan, Yan Program of Data Science Thompson Rivers University KamloopsBC Canada Department of Mathematics and Statistics Thompson Rivers University KamloopsBC Canada School of Computer Science and Technology Algoma University BramptonON Canada School of Computer Science University of Guelph GuelphON Canada

ISBN: (纸本)9798400717666

In the rapidly advancing field of genomics, the identification of Single Nucleotide Polymorphisms (SNPs) plays a crucial role in understanding complex phenotypic *** study introduces "PentaPen", an innovative computational workflow which combines the strengths of five penalized models to achieve improved accuracy in SNP *** compare the performance of PentaPen with existing models, highlighting its advantages in solving problems arising from when the number of predictors exceeds the number of *** model comparison, we provide insights into PentaPen's effectiveness in utilizing all SNPs as input, streamlines data pre-processing, and leverages parallel computation, enabling the workflow a considerable stride in SNP ***, a thorough evaluation and comparison of computational complexities signifies competitive edge of the workflow over individual penalized *** future research directions, we propose applications of PentaPen to plant-specific characteristics and suggest further explorations to assess the robustness of its *** summary, this manuscript presents the genomics community with a tool that combines computational efficiency with high-precision SNP detection, making a strong contribution to the field of genomic research. © 2024 Copyright held by the owner/author(s).

关键词： Genome

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Indonesian Agricultural-crops Classification Using Transfer Learning Model 8

Indonesian Agricultural-crops Classification Using Transfer ...

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8th International Conference on Computer science and Computational Intelligence, ICCSCI 2023

作者： Isnan, Mahmud Hidayat, Alam Ahmad Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNetB0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues. © 2023 The Authors. Published by Elsevier B.V.

关键词： Agriculture Classification Crop Deep Learning Transfer learning

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Count-data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events 8

Count-Data Mixed Models of Topical Tweets: A Case of Indones...

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8th International Conference on Computer science and Computational Intelligence, ICCSCI 2023

作者： Hidayat, Alam Ahmad Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data. © 2023 The Authors. Published by Elsevier B.V.

关键词： count data disaster surveillance floods mixed models Twitter

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