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arXiv 2018年

作者： Zhang, Lili Ray, Herman Priestley, Jennifer Tan, Soon Analytics and Data Science Ph.D. Program Kennesaw State University United States Analytics and Data Science Institute Kennesaw State University United States Ermas Consulting Inc. United States

Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit socring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with the best class weights can reduce the model bias and/or variance. From the perspective of the algorithm, cost-sensitive logistic regression is beneficial for increasing the value of predictors even if they are not in their optimized forms while maintaining monotonicity. From the perspective of predictors, the variable discretization performs better than cost-sensitive logistic regression, provides more reasonable coefficient estimates for predictors which have nonlinear relationships against their empirical logit, and is robust to penalty weights on misclassifications of events and non-events determined by their apriori proportions. Copyright © 2018, The Authors. All rights reserved.

关键词： Cost benefit analysis

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An Enhanced BERTopic Framework and Algorithm for Improving Topic Coherence and Diversity

An Enhanced BERTopic Framework and Algorithm for Improving T...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Sahil Sawant Jinhong Yu Kirtikumar Pandya Chun-Kit Ngan Rolf Bardeli Data Science Program Worcester Polytechnic Institute Worcester MA USA Big Data & Consumer Analytics Vodafone GmbH Duesseldorf NRW Germany

In this paper, we enhance and customize the existing BERTopic framework to develop and implement an automated pipeline that delivers a more coherent and diverse set of topics with an even moderate dataset. More specifically, the contributions of this work are threefold: (1) integrate a dynamic and advanced optimizer into the existing BERTopic framework to learn the optimal number of dimensions of different document embeddings, (2) develop a k-means-based algorithm in the optimizer to support the dimension-embedding learning, and (3) conduct an extensive experimental study on three distinct types of datasets, including DBPedia, AG News, and Reuters, to evaluate the performance of our approach in terms of the topic quality (TQ) score computed by the topic coherence and the topic diversity. From the results, we can conclude that our enhanced, automated BERTopic framework with its dimension-embedding learning algorithm on documents outperforms the TQ score of the existing framework by 4.49% (before removing the stop words) and 16.52% (after removing the stop words) among all the four representable document-embedding approaches, including the BERTopic's Default Sentence Transformer, Google's Universal Sentence Encoder, OpenAI GPT-2, and our investigators' developed Context-aware Embedding Model, on all the three datasets.

关键词： Heuristic algorithms Pipelines Coherence Transformers Internet Context modeling

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The effect of organic acid hydrolysis on the properties of nanocellulose from edamame husk (Glycine max (L.) merill)

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Wahyudi, Muhammad Sapto Holilah Bahruji, Hasliza Prasetyoko, Didik Asranudin Pratama, Agus Wedi Indriani, Dina Wahyu Suryanegara, Lisman Faradilla, RH Fitri Mahardika, Melbi Wardani, Ratih Kusuma Piluharto, Bambang Knight, Victor Feizal Norrrahim, Mohd Nor Faiz Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong Indonesia Research Collaboration Center for Nanocellulose BRIN-Andalas University Padang Indonesia Study Program of Chemistry Faculty of Science and Technology UIN Sunan Kalijaga Yogyakarta Indonesia Centre of Advanced Material and Energy Sciences Universiti Brunei Darussalam Jl. Tungku Link BE 1410 Brunei Darussalam Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Research Center for Applied Microbiology National Research and Innovation Agency (BRIN) Cibinong Indonesia Department of Biosystem Engineering Faculty of Agricultural Technology Universitas Brawijaya Jalan Veteran Malang Indonesia Department of Food Science and Technology Faculty of Agriculture Universitas Halu Oleo Kendari Indonesia Academy Pharmacy of Surabaya Ketintang Gayungan Surabaya 60231 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences University of Jember Jember Indonesia Research Center for Chemical Defence Defence Research Institute Universiti Pertahanan Nasional Malaysia Kem Sungai Besi Kuala Lumpur 57000 Malaysia

The increasing demand for sustainable materials has highlighted nanocellulose as a promising candidate due to its exceptional mechanical strength, thermal stability, and superior barrier properties. These attributes enable its application in diverse fields, including packaging, biomedical devices, and nanocomposites. This study investigates the valorization of edamame bean husks, a byproduct of the globally consumed legume snack, as a sustainable feedstock for nanocellulose production. Cellulose was extracted from the husks via delignification and bleaching, followed by a green and facile acid hydrolysis method employing organic acids (maleic acid, lactic acid, citric acid, and formic acid) to produce nanocellulose. The resulting nanocellulose exhibited a fibrillar morphology with particle sizes ranging from 7.59 to 17.51 nm, depending on the acid used. X-ray diffraction analysis revealed a crystallinity index ranging from 63.49 % to 67.24 %. Notably, acid hydrolysis significantly enhanced the thermal stability of the nanocellulose compared to both the raw husk and isolated cellulose. These findings demonstrate the potential of edamame agricultural waste as a valuable resource for sustainable and eco-friendly nanocellulose production, contributing to a circular bioeconomy. © 2025 The Authors

关键词： Characterization Edamame husk waste Green synthesis Nanocellulose Organic acids

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Influence of the event rate on discrimination abilities of bankruptcy prediction models

arXiv

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arXiv 2018年

作者： Zhang, Lili Priestley, Jennifer Ni, Xuelei Program in Analytics and Data Science Kennesaw State University Georgia United States Analytics and Data Science Institute Kennesaw State University Georgia United States Department of Statistics Kennesaw State University Georgia United States

In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. Under different event rates, models were comprehensively evaluated and compared based on Kolmogorov-Smirnov Statistic, accuracy, F1 score, Type I error, Type II error, and ROC curve on the hold-out dataset with their best probability cut-offs. Results show that Bayesian Network is the most insensitive to the event rate, while Support Vector Machine is the most sensitive. Copyright © 2018, The Authors. All rights reserved.

关键词： Bayesian networks

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Evolution, Sympatric Speciation, and Risk Aversion

Evolution, Sympatric Speciation, and Risk Aversion

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Oluwatobi I. Ajagbe Dean Frederick Hougen Data Science and Analytics Program University of Oklahoma Norman Oklahoma USA School of Computer Science University of Oklahoma Norman Oklahoma USA

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

Speciation is a crucial topic in the biological sciences. Knowledge of this process helps explain the diversity of life. In the computational realm, considerable efforts have been made to understand mechanisms and conditions that lead to speciation. Speciation has profound implications for artificial life, evolutionary computation, and evolutionary robotics, yet many aspects of it remain unexplored. Resource distribution is one of the key factors in natural selection, which is critical for sympatric speciation. Research on speciation typically assumes that there is a consistent supply of resources in each available niche. However, fluctuations in available resources might have strong impacts on the extent to which niches are occupied. Such fluctuations could be seen as analogous to risk in economics. Moreover, just as risk necessarily leads to risk aversion in reinforcement learning agents, so might fluctuations in resources lead to a phenomenon analogous to risk aversion in evolution. In this study, the effects of fluctuations in resource distribution are investigated in a simulated biological setting that has been established to favor sympatric speciation. We conclude that fluctuations in resource distribution lead to risk aversion in the process of evolution.

关键词： Birds Beak Statistics Sociology Evolution (biology) Standards Resource management

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

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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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Pressure-dependent electronic properties in the rocksalt structure of lithium fluoride: A DFT Study 5

Pressure-dependent electronic properties in the rocksalt str...

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5th Padjadjaran International Physics Symposium, PIPS 2022

作者： Gunawan, A.F. Astuti, F. Physics Study Program Republic of Indonesia Defense University Kawasan Indonesia Peace and Security Center Sentul Bogor16810 Indonesia Department of Physics Faculty of Science Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia

Density functional theory (DFT) calculation are carried out for the cubic structure of lithium fluoride (LiF). Variation of applied pressure is considered, ranging from 0 to 120 GPa with an increment of 30 GPa based on PBE and SCAN functional scheme. In zero pressure and energy-cut off 100 Ry, best result on lattice parameter is obtained by using PBE functional with the value of 4.06 Å, while SCAN functional overestimates the lattice parameter at 4.15 Å. The insulating gap of both results is significantly underestimated with 8.82 eV and 9.26 eV for PBE and SCAN functionals respectively. With the applied hydrostatic pressure, the lattice parameter are reduced exponentially, while still retaining the insulating behaviour with an increased value of gap energy. © Published under licence by IOP Publishing Ltd.

关键词： Lattice constants

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Investigation of the hexagonal boron nitride (h-BN) electronic and structural parameters and its dependency on the known semi-local functionals 5

Investigation of the hexagonal boron nitride (h-BN) electron...

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5th Padjadjaran International Physics Symposium, PIPS 2022

作者： Tiana, W.R. Ramadhan, M.R. Astuti, F. Physics Study Program Republic of Indonesia Defense University Kawasan Indonesia Peace and Security Center Sentul Bogor16810 Indonesia Department of Physics Faculty of Science Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia

Known as the next wonder material after graphene, hexagonal form of Boron-Nitride (hBN) attracts a lot of interest due to its capabilities to be utilized in wide range of applications. Here we investigate bilayer structure of hBN using density functional theory (DFT) techniques. In pristine case, we observed that the SCAN functional gives a significantly better estimation of inter-layer distance compared to the PBE functional. Furthermore, we also modelled the hBN structure with a single oxygen molecule sandwiched in between of B-N layer, and conclude that the most probable sites for oxygen molecule to be trapped is at the exact center of the bilayer hBN structure. © Published under licence by IOP Publishing Ltd.

关键词： Boron nitride

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Cholesterol allosteric modulation of the oxytocin receptor

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Biophysical Journal 2025年

作者： Marlow, Brennica Vogel, Alexander Kuenze, Georg Pankonin, Maik Reinhardt, Franziska Stadler, Peter F. Hildebrand, Peter W. Meiler, Jens Center for Structural Biology Vanderbilt University Nashville TN United States Institute for Drug Discovery Medical Faculty Leipzig University Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Leipzig University Leipzig Germany Interdisciplinary Center for Bioinformatics Leipzig University Leipzig Germany Chemical and Physical Biology Program Vanderbilt University Nashville TN United States Department of Chemistry Vanderbilt University Nashville TN United States Institute of Medical Physics and Biophysics Medical Faculty Leipzig University Leipzig Germany Bioinformatics Group Institute of Computer Science Interdisciplinary Center of Bioinformatics Leipzig University Leipzig Germany Max-Planck-Institute for Mathematics in the Sciences Leipzig Germany Institute of Theoretical Chemistry University of Vienna Wien Austria Facultad de Ciencias Universidad National de Colombia Sede Bogota Colombia Santa Fe Institute New Mexico Santa Fe Mexico

G-protein coupled receptors are critical components in cellular signaling, mediating various physiological responses to external stimuli. Here, we investigate the intricate relationship between cholesterol and the oxytocin receptor (OXTR), focusing on the binding mechanisms and the allosteric cross talk of bound cholesterol to the orthosteric ligand binding pocket. Utilizing molecular docking and molecular dynamics simulations, we identify cholesterol binding sites both on the agonist-bound and antagonist-bound state, which show differing distributions and residence times of the cholesterol molecules. Importantly, both methods converge on several key sites, demonstrating strong predictive overlap. Notably, one such site, and several sites detected by our molecular dynamics approach, also coincide with electron density observed in an experimental cryo-EM map, providing orthogonal validation for computational predictions. Allosteric network analysis uncovers the distinct pathways through which cholesterol may affect ligand-mediated receptor signaling, highlighting the significance of one site on the extracellular leaflet between TM4 and TM5, and two sites on the intracellular leaflet between TM2, TM3, and TM4 and between TM4 and TM5 in transmitting allosteric signals to the orthosteric pocket. These findings provide insights into the impact of cholesterol on OXTR function, emphasizing specific binding sites and signaling paths for further experimental exploration. © 2025 Biophysical Society

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A Novel Machine Learning Classifier Based on Genetic Algorithms and data Importance Reformatting

arXiv

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arXiv 2024年

作者： Alkhayyat, A.K. Hewahi, N.M. Big Data Science and Analytics Program College of Science University of Bahrain Bahrain Department of Computer Science College of Information Technology University of Bahrain Bahrain

In this paper, a novel classification algorithm that is based on data Importance (DI) reformatting and Genetic Algorithms (GA) named GADIC is proposed to overcome the issues related to the nature of data which may hinder the performance of the Machine Learning (ML) classifiers. GADIC comprises three phases which are data reformatting phase which depends on DI concept, training phase where GA is applied on the reformatted training dataset, and testing phase where the instances of the reformatted testing dataset are being averaged based on similar instances in the training dataset. GADIC is an approach that utilizes the exiting ML classifiers with involvement of data reformatting, using GA to tune the inputs, and averaging the similar instances to the unknown instance. The averaging of the instances becomes the unknown instance to be classified in the stage of testing. GADIC has been tested on five existing ML classifiers which are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), and Naïve Bayes (NB). All were evaluated using seven open-source UCI ML repository and Kaggle datasets which are Cleveland heart disease, Indian liver patient, Pima Indian diabetes, employee future prediction, telecom churn prediction, bank customer churn, and tech students. In terms of accuracy, the results showed that, with the exception of approximately 1% decrease in the accuracy of NB classifier in Cleveland heart disease dataset, GADIC significantly enhanced the performance of most ML classifiers using various datasets. In addition, KNN with GADIC showed the greatest performance gain when compared with other ML classifiers with GADIC followed by SVM while LR had the lowest improvement. The lowest average improvement that GADIC could achieve is 5.96%, whereas the maximum average improvement reached 16.79%. © 2024, CC BY.

关键词： Adversarial machine learning

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