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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Optimal Transfer Learning for Missing Not-at-Random Matrix Completion

arXiv

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arXiv 2025年

作者： Jalan, Akhil Jedra, Yassir Mazumdar, Arya Mukherjee, Soumendu Sundar Sarkar, Purnamrita UT Austin United States MIT United States UC San Diego United States Indian Statistical Institute Kolkata India Department of Computer Science LIDS United States Halıcıoğlu Data Science Institute Department of Computer Science and Engineering United States Department of Statistics and Data Sciences

We study transfer learning for matrix completion in a Missing Not-at-Random (MNAR) setting that is motivated by biological problems. The target matrix Q has entire rows and columns missing, making estimation impossible without side information. To address this, we use a noisy and incomplete source matrix P, which relates to Q via a feature shift in latent space. We consider both the active and passive sampling of rows and columns. We establish minimax lower bounds for entrywise estimation error in each setting. Our computationally efficient estimation framework achieves this lower bound for the active setting, which leverages the source data to query the most informative rows and columns of Q. This avoids the need for incoherence assumptions required for rate optimality in the passive sampling setting. We demonstrate the effectiveness of our approach through comparisons with existing algorithms on real-world biological datasets. © 2025, CC BY.

关键词： Contrastive Learning

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Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation

arXiv

引用

arXiv 2024年

作者： Islam, Md. Farhadul Zabeen, Sarah Manab, Meem Arafat Mahin, Mohammad Rakibul Hasan Mondal, Joyanta Jyoti Reza, Md. Tanzim Hasan, Md Zahidul Haque, Munima Sadeque, Farig Noor, Jannatun Research Group Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Biotechnology Program Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer and Information Sciences University of Delaware United States School of Law and Government Dublin City University Ireland Gina Cody School of Engineering and Computer Science Concordia University Canada

The majority of medical images, especially those that resemble cells, have similar characteristics. These images, which occur in a variety of shapes, often show abnormalities in the organ or cell region. The convolution operation possesses a restricted capability to extract visual patterns across several spatial regions of an image. The involution process, which is the inverse operation of convolution, complements this inherent lack of spatial information extraction present in convolutions. In this study, we investigate how applying a single layer of involution prior to a convolutional neural network (CNN) architecture can significantly improve classification and segmentation performance, with a comparatively negligible amount of weight parameters. The study additionally shows how excessive use of involution layers might result in inaccurate predictions in a particular type of medical image. According to our findings from experiments, the strategy of adding only a single involution layer before a CNN-based model outperforms most of the previous *** Codes 68T45 © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

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An efficient swarm intelligence approach to the optimization on high-dimensional solutions with cross-dimensional constraints, with applications in supply chain management

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Frontiers in Computational Neuroscience 2024年 18卷 1283974页

作者： Liu, Hsin-Ping Phoa, Frederick Kin Hing Chen-Burger, Yun-Heh Lin, Shau-Ping Data Science Degree Program National Taiwan University Taipei Taiwan Institute of Statistical Science Academia Sinica Taipei Taiwan Department of Computer Sciences Heriot-Watt University Edinburgh United Kingdom Institute of Biotechnology National Taiwan University Taipei Taiwan

Introduction: The Swarm Intelligence Based (SIB) method has widely been applied to efficient optimization in many fields with discrete solution domains. E-commerce raises the importance of designing suitable selling strategies, including channel- and direct sales, and the mix of them, but researchers in this field seldom employ advanced metaheuristic techniques in their optimization problem due to the complexities caused by the high-dimensional problems and cross-dimensional constraints. Method: In this work, we introduce an extension of the SIB method that can simultaneously tackle these two challenges. To pursue faster computing, CPU parallelization techniques are employed for algorithm acceleration. Results: The performance of the SIB method is examined on the problems of designing selling schemes in different scales. It outperforms the Genetic Algorithm (GA) in terms of both the speed of convergence and the optimized capacity as measured using improvement multipliers. Copyright © 2024 Liu, Phoa, Chen-Burger and Lin.

关键词： Supply chain management

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Approximately Optimal Search on a Higher-dimensional Sliding Puzzle

arXiv

引用

arXiv 2024年

作者： Merleau, Nono S.C. O’Malley, Miguel Roldán, Érika Mukherjee, Sayan Center for Scalable Data Analytics and Artificial Intelligence Department of Computer Science Universität Leipzig Germany Max Planck Institute for Mathematics in the Sciences Germany Duke University Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics United States

Higher-dimensional sliding puzzles are constructed on the vertices of a d-dimensional hypercube, where 2d−l vertices are distinctly coloured. Rings with the same colours are initially set randomly on the vertices of the hypercube. The goal of the puzzle is to move each of the 2d − l rings to pre-defined target vertices on the cube. In this setting, the k-rule constraint represents a generalisation of edge collision for the movement of colours between vertices, allowing movement only when a hypercube face of dimension k containing a ring is completely free of other rings. Starting from an initial configuration, what is the minimum number of moves needed to make ring colours match the vertex colours? An algorithm that provides us with such a number is called God’s algorithm. When such an algorithm exists, it does not have a polynomial time complexity, at least in the case of the 15-puzzle corresponding to k = 1 in the cubical puzzle. This paper presents a comprehensive computational study of different scenarios of the higher-dimensional puzzle. A benchmark of three computational techniques, an exact algorithm (the A* search) and two approximately optimal search techniques (an evolutionary algorithm (EA) and reinforcement learning (RL)) is presented in this work. The experiments show that all three methods can successfully solve the puzzle of dimension three for different face dimensions and across various difficulty levels. When the dimension increases, the A* search fails, and RL and EA methods can still provide a generally acceptable solution, i.e. a distribution of a number of moves with a median value of less than 30. Overall, the EA method consistently requires less computational time, while failing in most cases to minimise the number of moves for the puzzle dimensions d = 4 and d = 5. © 2024, CC BY.

关键词： Evolutionary algorithms

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Transfer Learning for Latent Variable Network Models

arXiv

引用

arXiv 2024年

作者： Jalan, Akhil Mazumdar, Arya Mukherjee, Soumendu Sundar Sarkar, Purnamrita Department of Computer Science UT Austin United States Halıcıoğlu Data Science Institute Dept of CSE UC San Diego United States Indian Statistical Institute Kolkata India Department of Statistics & Data Sciences UT Austin United States

We study transfer learning for estimation in latent variable network models. In our setting, the conditional edge probability matrices given the latent variables are represented by P for the source and Q for the target. We wish to estimate Q given two kinds of data: (1) edge data from a subgraph induced by an o(1) fraction of the nodes of Q, and (2) edge data from all of P. If the source P has no relation to the target Q, the estimation error must be Ω(1). However, we show that if the latent variables are shared, then vanishing error is possible. We give an efficient algorithm that utilizes the ordering of a suitably defined graph distance. Our algorithm achieves o(1) error and does not assume a parametric form on the source or target networks. Next, for the specific case of Stochastic Block Models we prove a minimax lower bound and show that a simple algorithm achieves this rate. Finally, we empirically demonstrate our algorithm’s use on real-world and simulated network estimation problems. © 2024, CC BY-NC-ND.

关键词： Errors

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Meta-Posterior Consistency for the Bayesian Inference of Metastable Systems

arXiv

引用

arXiv 2024年

作者： Adams, Zachary P. Mukherjee, Sayan Freie Universität Berlin Department of Mathematics and Computer Science Center for Scalable Data Analytics and Artificial Intelligence Universität Liepzig Germany Center for Scalable Data Analytics and Artificial Intelligence Department of Computer Science Universität Liepzig Germany Max Planck Institute for Mathematics in the Natural Sciences Duke University Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics United States

The vast majority of the literature on learning dynamical systems or stochastic processes from time series has focused on stable or ergodic systems, for both Bayesian and frequentist inference procedures. However, most real-world systems are only metastable, that is, the dynamics appear to be stable on some time scale, but are in fact unstable over longer time scales. Consistency of inference for metastable systems may not be possible, but one can ask about metaconsistency: Do inference procedures converge when observations are taken over a large but finite time interval, but diverge on longer time scales? In this paper we introduce, discuss, and quantify metaconsistency in a Bayesian framework. We discuss how metaconsistency can be exploited to efficiently infer a model for a sub-system of a larger system, where inference on the global behavior may require much more data. We also discuss the relation between metaconsistency and the spectral properties of the model dynamical system in the case of uniformly ergodic *** Codes 62F15, 60J70 © 2024, CC BY.

关键词： Stochastic systems

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Comparison of Decision Tree and Support Vector Machine for Predicting Jakarta Air Quality Index

Comparison of Decision Tree and Support Vector Machine for P...

引用

computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Musyaffa Ayman Rafif Gede Sanjaya Indrajaya Muhammad Kent Al-Ghazi Johnny Johnny Noviyanti T M Sagala Data Science Program School of Computer Sciences Bina Nusantara University Jakarta Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The people in the capital city of the Republic of Indonesia, Jakarta, have been living for years with air pollution. Air quality has been a concern for a long time due to the health risks it has on people, especially those at risk. Using classification algorithms, we would like to implement data mining for the air quality index and make predictions from our capital city, DKI Jakarta. This research aims to compare Decision Tree and Support Vector Machine in predicting Air Quality Index in DKI Jakarta in 2020. The dataset was collected from the Jakarta Open data Web, and the results of this research highlight the effectiveness of data mining techniques in predicting air quality index levels in Jakarta and emphasize the importance of selecting the appropriate classification algorithm for data mining applications. This research shows that SVM, compared to the Decision tree, has a better result with an accuracy score of 87.86%. On the other hand, Decision Tree has a score of 90.56%.

关键词： Support vector machines Urban areas Support vector machine classification Predictive models Air pollution Classification algorithms Decision trees

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Optimal Physician Shared-Patient Networks and the Diffusion of Medical Technologies

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Journal of data science 2023年第3期21卷 578-598页

作者： Ran, Xin An, Chuankai Rockmore, Daniel N. Department of Biomedical Data Science The Dartmouth Institute for Health Policy and Clinical Practice Geisel School of Medicine at Dartmouth Lebanon 03756 NH United States Department of Biomedical Data Science The Dartmouth Institute for Health Policy and Clinical Practice and the Program in Quantitative Biomedical Sciences Geisel School of Medicine at Dartmouth Lebanon 03756 NH United States Research Institute of China Investment Corporation Beijing 100010 China Department of Mathematics Department of Computer Science Hanover 03755 NH United States The Santa Fe Institute Santa Fe 87501 NM United States

Social network analysis has created a productive framework for the analysis of the histories of patient-physician interactions and physician collaboration. Notable is the construction of networks based on the data of "referral paths"- sequences of patient-specific temporally linked physician visits - in this case, culled from a large set of Medicare claims data in the United States. Network constructions depend on a range of choices regarding the underlying data. In this paper we introduce the use of a five-factor experiment that produces 80 distinct projections of the bipartite patient-physician mixing matrix to a unipartite physician network derived from the referral path data, which is further analyzed at the level of the 2,219 hospitals in the final analytic sample. We summarize the networks of physicians within a given hospital using a range of directed and undirected network features (quantities that summarize structural properties of the network such as its size, density, and reciprocity). The different projections and their underlying factors are evaluated in terms of the heterogeneity of the network features across the hospitals. We also evaluate the projections relative to their ability to improve the predictive accuracy of a model estimating a hospital's adoption of implantable cardiac defibrillators, a novel cardiac intervention. Because it optimizes the knowledge learned about the overall and interactive effects of the factors, we anticipate that the factorial design setting for network analysis may be useful more generally as a methodological advance in network analysis. © 2023 The Author(s).

关键词： bipartite network directional information factorial design implantable cardiac defibrillators optimal bipartite-unipartite projection shared-patient physician network

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Bayesian Inference for Non-Synchronously Observed Diffusions

arXiv

引用

arXiv 2025年

作者： Jasra, Ajay Kamatani, Kengo Wu, Amin School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China Institute of Statistical Mathematics Tokyo190-0014 Japan Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We consider the problem of Bayesian inference for bi-variate data observed in time but with observation times which occur non-synchronously. In particular, this occurs in a wide variety of applications in finance, such as high-frequency trading or crude oil futures trading. We adopt a diffusion model for the data and formulate a Bayesian model with priors on unknown parameters along with a latent representation for the the so-called missing data. We then consider computational methodology to fit the model using Markov chain Monte Carlo (MCMC). We have to resort to time-discretization methods as the complete data likelihood is intractable and this can cause considerable issues for MCMC when the data are observed in low frequencies. In a high frequency observation frequencies we present a simple particle MCMC method based on an Euler–Maruyama time discretization, which can be enhanced using multilevel Monte Carlo (MLMC). In the low frequency observation regime we introduce a novel bridging representation of the posterior in continuous time to deal with the issues of MCMC in this case. This representation is discretized and fitted using MCMC and MLMC. We apply our methodology to real and simulated data to establish the efficacy of our methodology. © 2025, CC BY.

关键词： Crude oil

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