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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Karli Eka Setiawan Gregorius Natanael Elwirehardja Bens Pardamean Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

Precise predictions of indoor climate conditions are required in the implementation of Smart Solar Dryer Dome (SDD). Trend development of prediction models is discussed in this review from 15 selected research papers (2018–2022) on indoor climate prediction which was obtained from research paper databases The output shows that the most used model for predicting indoor climate is Artificial Neural Network (ANN), especially Recurrent Neural Network (RNN) such as LSTM and GRU. However, there are some potential methods such as Transformer, Combined Support Vector Machines (SVM)-Deep Learning, and sequence-to-sequence which could outperform other commonly used models. Based on findings various opportunities exist to improve the precision of indoor climate prediction, which can bring power consumption efficiency and others benefit to Smart SDD users. Such studies may further be explored to produce more accurate machine learning models.

关键词： Support vector machines Systematics Recurrent neural networks Power demand Bibliographies Artificial neural networks Predictive models

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Patient portals: Objectives, acceptance, and effects on health outcome - A scoping review of reviews 66

Patient portals: Objectives, acceptance, and effects on heal...

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Joint Conference of the 66th Annual Meeting of the German Association of Medical Informatics, Biometry, and Epidemiology e.V. (gmds) and the 13th Annual Meeting of the TMF - Technology, Methods, and Infrastructure for Networked Medical Research e.V. 2021

作者： Leb, I. Magnin, S. Prokosch, H.-U. Boeker, M. Friedrich-Alexander-University Erlangen-Nürnberg Germany Medical Data Integration Center Dept. for IT and Applied Medical Informatics University Hospital Tübingen Translational Bioinformatics University of Tübingen Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Technical University of Munich School of Medicine Medical Center Rechts der Isar Institute for Medical Informatics Statistics and Epidemiology-IMedIS Germany

ISBN: (纸本)9781643682068

Patient portals provide patients access to their electronic health record and other functions as secure messaging. For over a decade, more and more patient portals are developed for various settings. The aim of this scoping review of reviews is to systematically search the literature for existing reviews to provide an overview of patient portals' objectives, acceptance and effects on outcome. We followed the PRISMA Statement and its extension for scoping reviews, and searched for articles published in 2011 2021. The 19 included articles were considerably heterogeneous concentrating on health outcome or patient portal facilitators and barriers. © 2021 IOS Press. All rights reserved.

关键词： Records management

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Incorporating the Knowledge Distillation to Improve the EfficientNet Transfer Learning Capability

Incorporating the Knowledge Distillation to Improve the Effi...

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2020 International Conference on data Science and Its Applications, ICoDSA 2020

作者： Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728182353

Due to the Deep Learning requirement for a large training dataset, Transfer Learning has become a central method in the field of Computer Vision, which heavily used Deep Learning. Since the adoption of transfer learning in the field, the performance of the models in computer vision is significantly upgraded. The common transfer learning practice in computer vision research is to use state-of-the-art architectures in the ImageNet dataset as the backbone network. The best performing architecture in ImageNet is currently held by EfficientNet, which is a set of architectures that were progressively grown from a baseline architecture. By observing the EfficientNet development approach, it is natural to hypothesize that the smaller EfficientNet architecture is capable to contain the knowledge of the larger architecture. This hypothesis comes from the fact that the larger EfficientNet architecture was grown from the smaller architecture. Therefore, in this study, we experimented if it is beneficial to transfer the knowledge from the larger architecture to the smaller architecture of the EfficientNet. To achieve this goal, we proposed a transfer learning method that uses Knowledge Distillation as the knowledge transfer mechanism from the larger to smaller architecture. We found that the proposed method is able to upgrade the performance of each of the EfficientNet architecture. Several smaller architectures are even able to outperform the larger architecture that was trained using the standard transfer learning method. © 2020 IEEE.

关键词： Distillation

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on Computer System, Information Technology, and Electrical Engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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BjuIR:A multi-omics database with various tools for accelerating functional genomics research in Brassica juncea

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Plant Communications 2024年第8期5卷 96-99页

作者： Linna Zhang Jinyuan Xiao Congyuan Liang Yifan Chen Changchun Yu Xinle Zhao Jiawei Li Mingli Yan Qian Yang Hao Chen Zhongsong Liu Zhengjie Wan Zhiquan Yang Qing-Yong Yang National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops College of InformaticsHuazhong Agricultural UniversityWuhan 430070China Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data College of InformaticsHuazhong Agricultural UniversityWuhan 430070China National Key Laboratory of Crop Genetic Improvement Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhan 430070China Yazhouwan National Laboratory Sanya 572025China National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops College of Horticulture and Forestry SciencesHuazhong Agricultural UniversityWuhan 430070China Crop Research Institute Hunan Academy of Agricultural SciencesChangsha 410125China College of Agronomy Hunan Agricultural UniversityChangsha 410128China

Dear Editor,Advances in high-throughput omics technologies,along with methodologies for integrating multi-omics datasets,have substantially enhanced the efficiency of identifying candidate genes in breeding(Gusev et al.,2018;Gupta et al.,2019).However,this process is often complex and *** address this challenge,databases that integrate extensive data and enable convenient and efficient functional genomics studies are being developed(Ma et al.,2021;Yang et al.,2023).

关键词： functional integrating convenient

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Scalable Bayesian inference for the generalized linear mixed model

arXiv

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arXiv 2024年

作者： Berchuck, Samuel I. Medeiros, Felipe Mukherjee, Sayan Agazzi, Andrea Department of Biostatistics & Bioinformatics Statistical Science Duke University NC27710 United States Department of Ophthalmology University of Miami MiamiFL33136 United States Center for Scalable Data Analysis and Artificial Intelligence Universität Leipzig Leipzig04105 Germany Department of Mathematics Università di Pisa PI Pisa56127 Italy

The generalized linear mixed model (GLMM) is a popular statistical approach for handling correlated data, and is used extensively in applications areas where big data is common, including biomedical data settings. The focus of this paper is scalable statistical inference for the GLMM, where we define statistical inference as: (i) estimation of population parameters, and (ii) evaluation of scientific hypotheses in the presence of uncertainty. Artificial intelligence (AI) learning algorithms excel at scalable statistical estimation, but rarely include uncertainty quantification. In contrast, Bayesian inference provides full statistical inference, since uncertainty quantification results automatically from the posterior distribution. Unfortunately, traditional Bayesian inference algorithms, including Markov Chain Monte Carlo (MCMC), become computationally intractable in big data settings. In this paper, we introduce a statistical inference algorithm for GLMM that leverages the scalability of modern AI algorithms with guaranteed uncertainty quantification that accompanies Bayesian inference. Our algorithm is an extension of stochastic gradient MCMC with novel contributions that address the treatment of correlated data (i.e., intractable marginal likelihood) and proper posterior variance estimation. Through theoretical and empirical results, we establish our algorithm’s statistical inference properties, and apply the method in a large electronic health records database. Copyright © 2024, The Authors. All rights reserved.

关键词： Big data

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Time-series Analysis of Correlation between Climatic Parameters and Dengue Fever in Indonesia

Time-series Analysis of Correlation between Climatic Paramet...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Rudi Nirwantono Joko Pebrianto Trinugroho Digdo Sudigyo Alam Ahmad Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665473286

Dengue fever is mainly caused by female mosquitoes from the genus Aedes. It predominantly occurs in equatorial to sub-tropical regions. The weather factors play a significant role in the mosquito life cycle, so a study on the impact of climate parameters on dengue incidence is required. This study aimed to understand the statistical association between the annual fluctuation of dengue cases with climate factors in Indonesia. Climatic data, annual rainfall, and annual mean ambient temperature of Indonesia were obtained from *** from 2010 to 2020, while the data on the annual incidence of dengue fever in Indonesia were collected from the Indonesian Health Profile by the Ministry of Health of Indonesia from 2010 to 2020. The association between climatic factors with dengue cases was calculated with Spearman statistical test after the data was indicated as not normally distributed. In addition, we performed Poisson regression analysis to examine how the changes in climatic parameters affect the dengue cases. The rain precipitation was insignificantly correlated with the dengue incidence in Indonesia during the study time. In contrast, the ambient temperature statistically showed a strong association with the dengue cases. The association between climatic factors and the number of dengue cases suggests that the model would likely be prospectively applied to the dengue early warning system as a nationwide intervention to reduce the epidemic impact in Indonesia.

关键词： Epidemics Temperature distribution Rain Meteorological factors Multimedia systems Time series analysis Weather forecasting

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A systematic literature review of machine learning application in COVID-19 medical image classification

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Procedia computer science 2023年 216卷 749-756页

作者： Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 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.

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance.

关键词： COVID-19 classification disease machine learning medical images

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Lighter Student Engagement Recognition in a Classroom Environment Using Skeletal Keypoints

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第6期52卷 1997-2014页

作者： Tarigan, Gabriel Asael Elwirehardja, Gregorius Natanael Nugroho, Kuncahyo Setyo Pardamean, Bens Computer Science Department Binus Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta1140 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Student retention is crucial for educational institutions, influencing reputation, finances, and ranking metrics. Engagement, reflecting a student’s connection, interest, and effort, plays a vital role in learning, fostering critical thinking, and supporting retention. Recent advancements use students’ poses to predict engagement, providing valuable insights without disrupting the teaching-learning dynamic. The prevailing research trend leans toward employing multi-modal approaches, such as a combination of pose detection with object detection. However, current methods use out-of-date object detection methods and manual dataset creation, which is cumbersome, requiring thousands of manually annotated data. This problem is then addressed by proposing a novel method of student engagement detection, using a combination of You Only Look Once Version 8 Mini (YOLOv8m) and MediaPipe as state-of-the-art alternatives to improve both object detection and human pose estimation. The (YOLOv8m + MediaPipe) method surpasses the baseline (YOLOv4 + OpenPose) with higher accuracy (0.70 vs. 0.41) and lower cross-entropy loss (0.40 vs. 0.60) on the test set, confirmed by a statistically significant paired t-test. It also exhibits a remarkable speed advantage, around 16 times faster than the baseline in pose detection data collection rates. Despite not being designed for it, the proposed method achieves multiple keypoint detection, matching the baseline’s amount. © (2025), (International Association of Engineers). All rights reserved.

关键词： Object detection

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Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property Prediction

arXiv

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arXiv 2024年

作者： Kang, Linjia Zhou, Songhua Fang, Shuyan Liu, Shichao College of Informatics Huazhong Agricultural University Hubei Wuhan430070 China The College of Informatics Huazhong Agricultural University China Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big Data Key Laboratory of Smart Animal Farming Technology Ministry of Agriculture Huazhong Agricultural University China

Accurate prediction of molecular properties is crucial in drug discovery. Traditional methods often overlook that real-world molecules typically exhibit multiple property labels with complex correlations. To this end, we propose a novel framework, HiPM, which stands for Hierarchical Prompted Molecular representation learning framework. HiPM leverages task-aware prompts to enhance the differential expression of tasks in molecular representations and mitigate negative transfer caused by conflicts in individual task information. Our framework comprises two core components: the Molecular Representation Encoder (MRE) and the Task-Aware Prompter (TAP). MRE employs a hierarchical message-passing network architecture to capture molecular features at both the atom and motif levels. Meanwhile, TAP utilizes agglomerative hierarchical clustering algorithm to construct a prompt tree that reflects task affinity and distinctiveness, enabling the model to consider multi-granular correlation information among tasks, thereby effectively handling the complexity of multi-label property prediction. Extensive experiments demonstrate that HiPM achieves state-of-the-art performance across various multi-label datasets, offering a novel perspective on multi-label molecular representation learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecular docking

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