检索结果-内蒙古大学图书馆

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Implementations in Childhood Stunting Research: A Systematic Literature Review

Machine Learning Implementations in Childhood Stunting Resea...

引用

International Conference on Information Management and Technology (ICIMTech)

作者： Reza Rahutomo Gregorius Natanael Elwirehardja Mahmud Isnan Faisal Asadi Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

Childhood stunting is a condition anticipated to affect the growth potential of children under the age of five. With numerous stunting researches that have been conducted, stunting datasets are now widely available to facilitate stunting research. This provides an opportunity to implement machine learning (ML) principles to produce a broader insight or a novel technique in stunting prediction. A systematic literature review is necessary to discover the landscape of machine learning implementation in the application domain as a preliminary study for creating an effective research roadmap. This paper presents a systematic literature review (SLR) of 22 curated manuscripts that focuses on identifying the ML models applied in stunting research, as well as the datasets used in such studies that were published during 2017–2022. The SLR process found that ML principles have been applied in stunting research since 2017, and the diversity of ML implementation has become more varied in 2021–2022. In terms of ML models, XGBoost and Random Forest are recognized as the two most utilized models, and stunting prediction is the most common ML implementation. The majority of stunting research utilizing ML has been conducted in Indonesia. Although national survey data has been the most commonly utilized dataset in stunting research, researchers in Indonesia have shown a preference for utilizing data from regional or independent surveys. This study will be followed by developing a classifier model for stunted children using XGBoost and Random Forest algorithms. The model will be trained on a dataset generated from StuntingDB.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Technology Behind Genomic database

The Technology Behind Genomic Database

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Joko Pebrianto Trinugroho Anzaludin Samsinga Perbangsa Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

In this digital era, we are exposed to a large amount of data. This includes biological data, which stores information about living organisms, including Deoxyribonucleic acid (DNA), genes, and proteins. With the development of information technology and information system, most of available biological data are stored in an online public database. Many of the databases are free-access and easily used, which helps the users, especially researchers, to make use of the data. Among the known public biological databases are the University of California Santa Cruz (UCSC) Genome Browser database and the Rat Genome database (RGD). These two databases provide access to the biological data from different organisms. This paper aims to describe the technology of public biological databases. Also elucidated in this paper are the differences features between UCSC Genome Browser database and the RGD. Our results showed that the UCSC contains much more biological data and features than the RGD. However, the genome browser of UCSC has a complex display, while the RGD has a simple display. Overall, both databases give the users the option to choose the most suitable database for them.

关键词： Proteins databases Genomics DNA Rats Biology Organisms

来源：评论

学校读者我要写书评

暂无评论

Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach

引用

Procedia Computer Science 2023年 227卷 262-271页

作者： Gregorius Natanael Elwirehardja Teddy Suparyanto Miftakhurrokhmat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta Indonesia 55584 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 R2. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed.

关键词： Oil Palm Yield Machine Learning XGBoost XAI SHAP

来源：评论

学校读者我要写书评

暂无评论

Less is More: Reducing Overfitting in Deep Learning for EEG Classification

Less is More: Reducing Overfitting in Deep Learning for EEG ...

引用

Computers in Cardiology (CinC)

作者： Songchi Zhou Shijia Geng Jun Li Deyun Zhang Ziqian Xie Chuandong Cheng Shenda Hong Department of Bioinformatics and Biostatistics Shanghai Jiao Tong University Shanghai China HeartVoice Medical Technology Hefei China College of Electronic Science and Engineering Jilin University Changchun China University of Texas Health Science Center Houston TX US Department of Neurosurgery The First Affiliated Hospital of USTC Hefei China National Institute of Health Data Science Peking University Beijing China Institute of Medical Technology Health Science Center of Peking University Beijing China

Although most of the patients' recordings includes large scale long-term physiological time series, the patient-level quantity is relatively small, posing great challenges for machine learning methods. As part of the George B. Moody PhysioNet Challenge 2023, we aim to propose a series of Reducing Overfitting techniques in Deep learning for EEG (coined as RODE) in this scenario, for neurological recovery prognosis. RODE is a simple yet effective machine-learning method, which is mostly powered by generalizable deep-learning features. Specifically, we first pre-train a convolutional neural network on the segment level with a margin-based and mining-based loss, and extract deep features from it. Then we decrease the deep features' complexity using dimensionality reduction methods, which prove to be quite significant for reducing overfitting in deep learning. Finally, we combine the reduced features with static features in patient level, and put them into an ensemble model for classification. Our team, PKU_NIHDS, receives a Challenge score of 0.821 on the hidden validation set and 0.708 on the hidden test set.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Obstructive sleep apnea increases the risk of herpes zoster and postherpetic neuralgia

引用

Postgraduate Medicine 2024年第1期136卷 22-29页

作者： Wang, Chih-An Hsieh, Wen-Che Hsu, Tzu-Ju Tsai, Fuu-Jen Hsu, Chao-Yu Division of Respiratory Therapy Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan Department of Chinese Medicine Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan Management Office for Health Data Clinical Trial Research Center China Medical University Hospital Taichung Taiwan School of Chinese Medicine College of Chinese Medicine China Medical University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Division of Medical Genetics China Medical University Children’s Hospital Taichung Taiwan Department of Biotechnology and Bioinformatics Asia University Taichung Taiwan Department of Medical Education Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan Department of Artificial Intelligence and Healthcare Management Central Taiwan University of Science and Technology Taichung Taiwan Center for General Education National Taichung University of Science and Technology Taichung Taiwan Department of General Education National Chin-Yi University of Technology Taichung Taiwan

Objective: Diseases associated with chronic pain are typically a major source of stress for patients;and have been linked to herpes zoster (HZ) development. Here, we investigated whether obstructive sleep apnea (OSA) is a potential stressor that increases the risk of HZ and postherpetic neuralgia (PHN) in affected individuals. Methods: The data used in this study were obtained from the National Health Insurance Research database. The study cohort included patients aged between 20 and 100 years who had OSA during the period from 2000 to 2017 (with tracking completed until 2018). The case group and the control group were matched at a 1:1 ratio on the basis of age, sex, comorbidities, and index year, with patients who had outcomes before the index date being excluded. The outcomes considered in this study were HZ and PHN. The risk of HZ and PHN with and without OSA was calculated, and age, sex, comorbidities, and index year were adjusted for. Results: There were 25,211 patients in each group. Patients with OSA had a significantly higher risk of HZ (adjusted hazard ratio [aHR] = 1.22) than those without did. The patients with OSA had also a significantly higher risk of PHN (aHR = 1.36) than those without did. In term of comorbidities, the patients with OSA without (aHR = 1.28) and with (aHR = 1.17) comorbidities had a significantly higher risk of HZ compared with those without OSA. In addition, the patients with OSA but no other comorbidities (aHR = 1.68) had a significantly higher risk of PHN than those without did. Conclusion: OSA increases the risk of not only HZ but also PHN. Therefore, patients with OSA should be aware of the potential effect of the disease on their stress levels, as well as the increased risk of developing HZ and PHN. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： herpes zoster hypoxia Obstructive sleep apnea postherpetic neuralgia varicella zoster virus

来源：评论

学校读者我要写书评

暂无评论

Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

引用

Procedia Computer Science 2023年 216卷 57-66页

作者： Mira Hayati Kahlil Muchtar Roslidar Novi Maulina Irfan Syamsuddin Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar 90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.

关键词： Diabetic retinopathy Deep Learning CNN Image Enhancement CLAHE

来源：评论

学校读者我要写书评

暂无评论

Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

引用

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

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

来源：评论

学校读者我要写书评

暂无评论

Characterizing Emergency Department Care for Patients With Histories of Incarceration

引用

JACEP Open 2025年第1期6卷 100022-100022页

作者： Huang, Thomas Socrates, Vimig Ovchinnikova, Polina Faustino, Isaac Kumar, Anusha Safranek, Conrad Chi, Ling Wang, Emily A. Puglisi, Lisa Wong, Ambrose H. Wang, Karen H. Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Department for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States SEICHE Center for Health and Justice Yale School of Medicine New Haven CT United States Equity Research and Innovation Center Yale School of Medicine Yale University New Haven CT United States

Objectives: Patients with a history of incarceration experience bias from health care team members, barriers to privacy, and a multitude of health care disparities. We aimed to assess care processes delivered in emergency departments (EDs) for people with histories of incarceration. Methods: We utilized a fine-tuned large language model to identify patient incarceration status from 480,374 notes from the ED setting. We compared socio-demographic characteristics, comorbidities, and care processes, including disposition, restraint use, and sedation, between individuals with and without a history of incarceration. We then conducted multivariable logistic regression to assess the independent correlation of incarceration history and management in the ED while adjusting for demographic characteristics, health behaviors, presentation, and past medical history. Results: We found 1734 unique patient encounters with a history of incarceration from a total of 177,987 encounters. Patients with history of incarceration were more likely to be male, Black, Hispanic, or other race/ethnicity, currently unemployed or disabled, and had smoking and substance use histories, compared with those without. This cohort demonstrated higher odds of elopement (OR: 3.59 [95% CI: 2.41–5.12]), leaving against medical advice (OR: 2.39 [95% CI: 1.46–3.67]), and being subjected to sedation (OR: 3.89 [95% CI: 3.19–4.70]) and restraint use (OR: 3.76 [95% CI: 3.06–4.57]). After adjusting for covariates, the association between incarceration and elopement remained significant (adjusted odds ratio: 1.65 [95% CI: 1.08–2.43]), while associations with other dispositions, restraint use, and sedation did not persist. Conclusion: This study identified differences in patient characteristics and care processes in the ED for patients with histories of incarceration and demonstrated the potential of using natural language processing in measuring care processes in populations that are largely hidden, but highly prev

关键词： artificial intelligence emergency medicine incarceration large language models quality of health care

来源：评论

学校读者我要写书评

暂无评论

Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

Impact of CLAHE-based image enhancement for diabetic retinop...

引用

7th International Conference on Computer Science and Computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：