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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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A Bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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

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

ISBN: (纸本)9798350307306

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site. © 2023 IEEE.

关键词： Amino acids

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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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A Hybrid Inference Engine to Predict Uncertain Sugar Content for Sugar Industry 9th

A Hybrid Inference Engine to Predict Uncertain Sugar Content...

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9th International Conference on Computer science and Computational Intelligence, ICCSCI 2024

作者： Asrol, Muhammad Napthaleni Utama, Ditdit Nugeraha Suharjito Harito, Christian Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul05006 Korea Republic of

The sugar industry is facing challenges in increasing productivity to meet consumer demand. One opportunity for productivity improvement lies in ensuring sugar content. This study proposes a hybrid model to predict sugar content by considering uncertainty factors. A hybrid model combining fuzzy subtractive clustering, and a fuzzy inference system is proposed to predict sugar content. The clustering results using silhouette and fuzzy subtractive clustering successfully identified 6 cluster centres from 2225 datasets collected in a sugar industry in East Java Province. The hybrid inference engine model is designed with fuzzy rules derived from the clustered data. Two inference models are developed: triangular and Gaussian fuzzy numbers. The testing results indicate that the hybrid model with triangular fuzzy numbers shows the smallest error with an R2 value of 0.95. This model is possible to applied in the sugar industry for decision makers in improving productivity with taking attention into uncertain factors influencing sugar content. © 2024 The Authors.

关键词： Prediction models

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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...

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

作者： Rafif, Musyaffa Ayman Sanjaya Indrajaya, Gede Al-Ghazi, Muhammad Kent Johnny, Johnny Sagala, Noviyanti T M Bina Nusantara University School of Computer Sciences Data Science Program Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

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%. © 2023 IEEE.

关键词： Decision trees

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Fetal Health Risk Classification Using Important Feature Selection and Cart Model on Cardiotocography data

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 193-206页

作者： Ilham, Ahmad Nagara, Thahta Ardhika Prabu Kamaruddin, Mudyawati Khikmah, Laelatul Mantoro, Teddy Department of Informatics Universitas Muhammadiyah Semarang Semarang Indonesia Intelligent Data Science Research Group Universitas Muhammadiyah Semarang Semarang Indonesia Postgraduate Program of Medical Laboratory Science Universitas Muhammadiyah Semarang Semarang Indonesia Interdisciplinary Research Laboratory for Experimental Plasma Medicine Universitas Muhammadiyah Semarang Semarang Indonesia Department of Statistics Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang Semarang Indonesia School of Computer Science Nusa Putra University West Java Indonesia

Fetal mortality and newborn health issues require urgent attention because of high maternal and infant mortality rates during labor, highlighting the critical need for accurate fetal condition monitoring to reduce complications. This study proposes the development of a fetal health risk classification model based on Important Feature Selection (IFS) and a Classification and Regression Tree (CART) using cardiotocography (CTG) data from the UCI Machine Learning Repository. The IFS method was used to select the most relevant features, reduce model complexity, and increase generalization to prevent overfitting. The IFS-CART model was tested with 10-fold cross-validation and showed an accuracy of 94.50%, superior to the conventional CART, which only reached 93.83%. In addition, the average values of the True Positive Rate (TPR) and True Negative Rate (TNR) also increased, indicating that this model is effective in distinguishing normal, suspected, and pathological fetal conditions. Evaluation using the area under the curve receiver operating characteristic (AUC-ROC) showed that the model had high performance in detecting at-risk conditions, with an AUC of 0.981 for the"suspect" class. This finding confirmed that IFS-CART is not only accurate but also has high interpretability, making it easy for medical personnel to use for clinical decision support. The results of this study show that IFS-CART can serve as a reliable decision support system for real-time fetal health monitoring. Further implementation is expected to improve diagnostic accuracy and prevent complications during pregnancy and labor © 2025 Slovene Society Informatika. All rights reserved.

关键词： Fetal monitoring

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Comparison of K-Nearest Neighbor and Support Vector Regression for Predicting Oil Palm Yield 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Pardamean, Bens Suparyanto, Teddy Sinaga, Gokma Sahat Tua Elwirehardja, Gregorius Natanael Firmansyah, Erick Ginting, Candra Mawandha, Hangger Gahara Putra, Dian Pratama 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 Faculty of Agriculture Institute of Agriculture STIPER Yogyakarta55282 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

With the high demand for oil palm production, implementations of Machine Learning (ML) technologies to provide accurate predictions and recommendations to assist oil palm plantation management tasks have become beneficial, such as in predicting annual oil palm. However, different geographical and meteorological conditions may result in different scales of influence for each variable. In this research, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR) were used in predicting oil palm yield based on data collected in Riau, Indonesia. Pearson’s correlation coefficient was also calculated in selecting the input features for the models, whereas normalization and standardization were used in scaling the data. By setting the minimum absolute correlation threshold to 0.1 and using standardization, both models managed to obtain more than 0.81 R2, with SVR obtaining the overall best performance with 0.8709 R2, 1.372 MAE, and 1.8025 RMSE without hyperparameter fine-tuning. It was also discovered that the oil palm yield in the previous year is the variable with the most influence in estimating oil palm yield in the current year, followed by the number of plants and soil types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series data 8

Long Short-Term Memory-based Models for Sleep Quality Predic...

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

作者： Hidayat, Alam Ahmad Budiarto, Arif 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 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

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters. © 2023 The Authors. Published by Elsevier B.V.

关键词： deep learning forecasting sleep quality time series wearable devices

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