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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 98-109页

作者： Namdari, Himan Moradikia, Majid Zekavat, Seyed Askari, Radwin Mangoubi, Oren Petkie, Doug Data Science Program WorcesterMA01609 United States Michigan Technological University Department of Geological and Mining Engineering and Sciences HoughtonMI48105 United States Physics Department WorcesterMA01609 United States

In this article, we investigate an intelligent ground penetrating radar (GPR) that facilitates root-zone soil moisture estimation, a key parameter in precision agriculture. To create an intelligent GPR, we must train machine learning (ML) methods applied to the GPR-received signal. This process requires a large number of labeled GPR data that would be time-consuming and labor-intensive if created via field measurements. This article uses gprMAX software to emulate drone-coupled GPR received signal to generate large-scale data for training ML models. The data are created via a 1.5 GHz Ricker waveform considering a three-layer soil consistent with a realistic soil horizon model. The approach is structured as follows: first, we generate a synthetic dataset using gprMAX. Feature engineering techniques are then employed to extract meaningful components from the GPR signals, followed by a rigorous selection process to identify the most effective ML model for soil moisture prediction. Finally, we validate our model by integrating synthetic data with real GPR data collected at the SoilX lab at Worcester Polytechnic Institute, enhancing prediction accuracy and generalization capability. Our proposed model achieves an overall average root-mean-squared error of 0.5%, and 1.56 cm for moisture and depth estimations, respectively. The proposed intelligent GPR, when installed on a drone, enables high horizontal (e.g., 10 m) and vertical (e.g., 1.5 cm) resolution and high penetration depth (beyond 2 m) megafarm root-zone 3-D moisture map creation. Thus, it offers much higher capabilities when compared to traditional methods, such as synthetic aperture radar and satellite imaging. These results facilitate efficient farming practices, such as optimizing irrigation models, for better crop yields and food security. © 2023 IEEE.

关键词： Soil moisture

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Enhancing mortgage rate prediction: a comprehensive evaluation of computational statistical approaches

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International Journal of Computer Mathematics 2024年第4期101卷 373-385页

作者： Danlei Zhu Yousaf Khaliq Haoyuan Wang Tingting Sun Donglin Wang Department of Mathematical Science Computational and Data Science Program Middle Tennessee State University Murfreesboro TN USA

The noterate is a tool for predicting home mortgage rates, it is often skewed and has missing information. The noterate could be affected by incomplete or inaccurate data, thus leading to inaccurate predictions. Financial organizations including mortgage companies or banks need to consider the risk of uncertainty carefully and make a more accurate prediction based on some suitable models. To deal with this situation, in this paper we compared six computational statistical methods, including the ordinary least square model, maximum likelihood estimation, maximum a posterior, bootstrapping, Metropolis-Hastings, and Gibbs sampling method on a mortgage dataset. Based on thekfold cross-validation technique and four metrics including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), the bootstrapping method outperforms other methods. In practice, this method is recommended for predicting noterate.

关键词： Bootstrapping maximum likelihood estimation maximum a posterior Gibbs sampling Metropolis-Hastings 91-10 91-11 68T05 68T09 68T10

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An Ensemble-Based Weight-Learning Framework and Algorithm for Imputing Missing Values on High-Dimension Low-Sample Size datasets 23

An Ensemble-Based Weight-Learning Framework and Algorithm fo...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ngan, Chun-Kit Yang, Boyuan Worcester Polytechnic Institute Data Science Program WorcesterMA United States

ISBN: (纸本)9781665494571

High-Dimension, Low-Sample Size (HDLSS) datasets are the datasets that their sample sizes are much smaller than the number of feature dimensions. In spite of that, HDLSS datasets appear in many domains and organizations, including social work, medical fields, international travels, and chemical industry. As many HDLSS datasets are very difficult to be collected, their missing values raise many issues and reduce reliability on the datasets to solve a domain and an organizational problem. To address the issues of the missing values in HDLSS datasets, we propose an Ensemble-Based Weight-Learning (EBWF) framework. Given HDLSS datasets and available Machine Learning (ML) classifiers, the EBWF framework is an automated pipeline that can generate the best imputed dataset. To support the EBWF framework, we develop a novel Record Probability Weight Learning (RPWL) imputation algorithm that combines the data record probability calculation from each ML classifier and weight learning computations among all the ML classifiers to impute the missing values in the dataset. To show the effectiveness of our approach, we conduct an experimental case study and evaluation on a survey dataset, i.e., 212 international travelers in Houston for assessing sexual risk-taking behaviors, as a pilot study, where Houston is a critical transit point frequented by international visitors. Based upon the travelers' survey data, where the survey records' missing rate is in the range of 10% to 70%, and the imputed dataset generated by our approach, we show that the classification performance delivered by our framework and algorithm outperform the other baseline approaches'. © 2021 IEEE.

关键词： Chemical industry Machine learning algorithms Pipelines Organizations Machine learning Probability Classification algorithms

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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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Emotional Reactivity Classification Using Artificial Intelligence Based on Twitter in Bahasa Indonesia 3

Emotional Reactivity Classification Using Artificial Intelli...

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3rd IEEE International Conference on Social sciences and Intelligence Management, SSIM 2023

作者： Paramita, Greta V. Andangsari, Esther W. Kemala, Ade P. Ali, Moondore M. Putri, Trisa M. Puspita, Katarina I. Bina Nusantara University Faculty of Humanities Psychology Department Jakarta Indonesia Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350327786

Twitter is one of Indonesia's most widely used social media to express positive and negative emotions. Negative emotions are referred to as emotional reactivity, an example of which is depression. To detect emotional reactivity, Artificial Intelligence (AI) was used. With deep learning pretrained model methods (IndoBERT and IndoBERTweet), AI successfully classified emotional reactivity. AI satisfactorily detected emotional reactivity on Twitter. © 2023 IEEE.

关键词： Deep learning

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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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Air Pollution Prediction using Random Forest Classifier: A Case Study of DKI Jakarta

Air Pollution Prediction using Random Forest Classifier: A C...

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2023 International Conference on Computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Muljana, Richie Ayuningtyas, Lintang Diah Daksa, Rayhan Prawira Djamhari, Simen Ferdinand Fiezayyan, Muhammad Ariiq Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

This research paper analyzes the Air Pollution Standard Index (APSI) in Jakarta, Indonesia, using the Random Forest Classifier (RFC). The study aims to predict the APSI level in Jakarta based on the concentrations of various pollutants, including particulate matter (PM), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3). The data used in the study was collected from a public website called Jakarta Open data from January to December 2020. The SMOTE-Tomek technique was used to handle an imbalanced dataset in this work. The results show that the RFC model accurately predicts the APSI level in Jakarta with an accuracy of 95%. In addition, RFC can identify ozone (O3) and particulate matter (PM) are the most critical factors influencing the APSI level. This study provides valuable insights into the factors influencing air pollution in Jakarta and can be used to inform city decision-making regarding air quality management. This paper discusses the finding's significance and potential future research directions. © 2023 IEEE.

关键词： Carbon monoxide

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Salary Classification & Prediction based on Job Field and Location using Ensemble Methods

Salary Classification & Prediction based on Job Field and Lo...

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2023 International Conference on Computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Siswanto, Jocelyn Verna Castilani, Laurentia Alyssa Winata, Natasha Hartanti Nugraha, Nathania Christy Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The economy is one of the determinants of how a person can live their life. In this current economic situation, inflation occurs everywhere, causing the prices of necessities to rise. In order to have a decent life, people must find a job with the highest possible salary to fulfill their needs. Various job industries have their salary range. Obtaining the information of salary level for the respective job is helpful for employers and employees to estimate the expected salary. This work aims to classify the salary level of jobs available in Indonesia and determine whether those salaries are decent enough. The learning methods are logistic regression, decision tree, k-nearest neighbor, support vector machine, voting classifier, bagging classifier, random forest, and boosting classifier. Random Forest achieved the best result with an accuracy rate of 72%. Based on the analysis result, factors such as job field, educational background, working experience, working hours, and job location influence salary. © 2023 IEEE.

关键词： Decision trees

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Comparison of Different Machine Learning Algorithms for Predicting Loan Risk Categories

Comparison of Different Machine Learning Algorithms for Pred...

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2023 International Conference on Computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Sudjono, Salomo Hendrian Adrian, Favian H.N. Sunarya, Caroline Angelina Ariyanto, Gabrielle Felicia Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

There are many cases where borrowed money by debtors is not returned. It is because the company misjudged in determining the risk of lending. Thus, debtors cannot repay their debts and end up in losses on the company's side. Thus, this research aims to create an accurate classification model for determining loan risk categories to minimize business losses. We used the public dataset from kaggle website. Then we perform several data transformation and analysis methods are performed to obtain information from the data. We built 2 classification models to predict the original label. Unfortunately, the desired results did not achieve based on the f1-score. Therefore, the clustering method using K-Means is applied to make a new label for the dataset and determine the behavior of each cluster. Each cluster is labeled then build a model to perform cluster classification. We created several classification models and found that gradient tree boosting is the most accurate predictor of the loan risk category based on the accuracy. We used clustering to create a classification model that can have high accuracy. The classification model we built can have high accuracy because it uses clustering. The empirical results of this study and others pave the way for further in-depth future work to guarantee that the algorithm performs acceptably when confronted with more complicated real data. © 2023 IEEE.

关键词： K-means clustering

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Hidden or Inferred: Fair Learning-to-Rank with Unknown Demographics 24

Hidden or Inferred: Fair Learning-to-Rank with Unknown Demog...

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Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society

作者： Oluseun Olulana Kathleen Cachel Fabricio Murai Elke Rundensteiner Data Science Program Worcester Polytechnic Institute Worcester MA Data Science Program Worcester Polytechnic Institute Worcester MA and Computer Science Department Worcester Polytechnic Institute Worcester MA

As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (LTR) models. These models rely on the availability of sensitive demographic features such as race or sex. However, in practice, regulatory obstacles and privacy concerns protect this data from collection and use. As a result, practitioners may either need to promote fairness despite the absence of these features or turn to demographic inference tools to attempt to infer them. Given that these tools are fallible, this paper aims to further understand how errors in demographic inference impact the fairness performance of popular fair LTR strategies. In which cases would it be better to keep such demographic attributes hidden from models versus infer them? We examine a spectrum of fair LTR strategies ranging from fair LTR with and without demographic features hidden versus inferred to fairness-unaware LTR followed by fair re-ranking. We conduct a controlled empirical investigation modeling different levels of inference errors by systematically perturbing the inferred sensitive attribute. We also perform three case studies with real-world datasets and popular open-source inference methods. Our findings reveal that as inference noise grows, LTR-based methods that incorporate fairness considerations into the learning process may increase bias. In contrast, fair re-ranking strategies are more robust to inference errors. All source code, data, and experimental artifacts of our experimental study are available here: https://***/sewen007/***

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