检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Effectiveness of Transformer Models for Molecular Property Prediction: A Review

Evaluating the Effectiveness of Transformer Models for Molec...

引用

Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Alyssa Imani Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Transformer models, originally successful in natural language processing, are now being applied to chemical and biological studies, excelling in areas such as molecular property prediction, material science, and drug discovery. BERT, a Transformer-based model, has become foundational in cheminformatics, particularly for QSAR (Quantitative Structure-Activity Relationship) modeling and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluations in drug discovery. However, achieving higher accuracy often requires designing more complex models, which can compromise their interpretability. This posing a challenge for researchers who need to understand the reasoning behind the predictions. The trade-off between accuracy and interpretability presents a critical challenge in applying black box models to real-world problems in cheminformatics. This work compares Transformer-based models with traditional machine learning and deep learning approaches, focusing on both interpretability and performance. The goal is to highlight the strengths and limitations of each method, offering insights into their optimal use in drug discovery and material science.

关键词： Accuracy Toxicology Reviews Biological system modeling Predictive models Transformers Natural language processing Drug discovery Metabolism Information technology

来源：评论

学校读者我要写书评

暂无评论

Implementation of expert systems in potassium deficiency in cocoa plants using forward chaining method 7

Implementation of expert systems in potassium deficiency in ...

引用

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

作者： Hafizal, Muhammad Titan Putra, Dian Pratama Wirianata, Herry Nugraha, Nanda Satya Suparyanto, Teddy Hidayat, Alam Ahmad Pardamean, Bens Faculty of Agriculture STIPER Institute of Agriculture Yogyakarta55282 Indonesia Faculty of Forestry STIPER Institute of Agriculture Yogyakarta55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As one of the largest exporters in the world, cocoa (Theobroma cacao L.) production in Indonesia provides an important contribution to the plantation sector that can, directly and indirectly, attribute to the national economic development. However, recent data shows a decline in cocoa productivity in Indonesia. This is arguably caused by various factors, including poor agricultural management and practices in cocoa plantations, that may lead to lower fertility in the soil and an increased risk of diseases and pests. Potassium deficiency is a major contributing factor to the low soil fertility that affects cocoa yields. Therefore, in this work, we implement an application with an expert system utilizing the forward chaining method to detect potassium deficiency in cocoa plants and then give a fertilization-based recommendation based on the plants' condition. The system employs a set of rules to identify symptoms related to the deficiency on the sample photo of a cocoa leaf according to the channels of red, green, and blue of the image. The sample images of cocoa leaves are submitted to the application with an easy-To-use interface that can show the scanning result and proceed to display the suggested quantity of fertilizers to prevent potassium deficiency. Implementing the system can contribute constructive impacts to improve current practices in the overall cropping system of cocoa plants. © 2022 Elsevier B.V.. All rights reserved.

关键词： Expert systems

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Lightweight Pedestrian Detection through Guided Filtering and Deep Learning 10

Lightweight Pedestrian Detection through Guided Filtering an...

引用

10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge Encoder-Decoder Framework

引用

Procedia Computer Science 2024年 245卷 391-398页

作者： Matthew Martianus Henry Nur Adhianti Heryanto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The growing number of medical images has led to radiologist burnout, which seriously impacts the radiologist's performance. To address the previously mentioned issue, an Auxiliary Signal Guided Knowledge (ASGK) multimodal encoder-decoder framework was designed to automatically generate the medical report based on the proposed medical graph and natural language decoder. It utilizes DenseNet-121 as the image encoder. With DenseNet-121 lack of computational and memory efficiency, this study aims to explore the potential of EfficientNetB0 to EfficientNetB4 as an ASGK image encoder substitute. The framework is trained with IU X-Ray dataset for 30 epochs, with Adam optimizer, a learning rate of 0.01 with 0.8 decay rate, binary cross entropy loss for the medical tags, and cross-entropy loss for the generated medical captions. During the framework training process with each image encoder, the parameter that achieves the highest CIDEr score on the validation set is considered the best image encoder parameter and will be used on the test set. On the test set, EfficientNetB3 as an ASGK image encoder has been shown to increase the CIDEr score to 0.35, a significant increase from the 0.28 CIDEr score obtained by the ASGK using DenseNet-121. This score is only a 1% decrease from the best validation score. It suggests that not only EfficientNetB3 increases the framework's performance, it is also less prone to overfitting. This study has demonstrated that EfficientNetB3 is a potential image encoder substitute for DenseNet-121 in the ASGK framework.

关键词： Automated medical report generation DenseNet-121 EfficientNet Encoder-decoder framework

来源：评论

学校读者我要写书评

暂无评论

Biomedical data and AI

引用

Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dysregulated mechanisms underlying Duchenne muscular dystrophy from co-expression network preservation analysis

引用

BMC Research Notes 2015年第1期8卷 1-10页

作者： Mukund, Kavitha Subramaniam, Shankar Bioinformatics and System Biology Graduate Program University of California San Diego MC0412 9500 Gilman Drive San Diego 92093 CA United States Departments of Bioengineering Computer Science and Engineering Cellular and Molecular Medicine Chemistry and Biochemistry University of California San Diego 9500 Gilman Drive San Diego 92093 CA United States

Background: Duchenne Muscular Dystrophy (DMD) is an X-linked recessive disorder with its primary insult on the skeletal muscle. Severe muscle wasting, chronic inflammation and fibrosis characterize dystrophic muscle. Here we identify dysregulated pathways in DMD utilizing a co-expression network approach as described in Weighted Gene Co-expression Network Analysis (WGCNA). Specifically, we utilize WGCNA's "preservation" statistics to identify gene modules that exhibit a weak conservation of network topology within healthy and dystrophic networks. Preservation statistics rank modules based on their topological metrics such as node density, connectivity and separability between networks. Methods: Raw data for DMD was downloaded from Gene Expression Omnibus (GSE6011) and suitably preprocessed. Co-expression networks for each condition (healthy and dystrophic) were generated using the WGCNA library in R. Preservation of healthy network edges was evaluated with respect to dystrophic muscle and vice versa using WGCNA. Highly exclusive gene pairs for each of the low preserved modules within both networks were also determined using a specificity measure. Results: A total of 11 and 10 co-expressed modules were identified in the networks generated from 13 healthy and 23 dystrophic samples respectively. 5 out of the 11, and 4 out of the 10 modules were identified as exhibiting none-to-weak preservation. Functional enrichment analysis identified that these weakly preserved modules were highly relevant to the condition under study. For instance, weakly preserved dystrophic module D2 exhibited the highest fraction of genes exclusive to DMD. The highly specific gene pairs identified within these modules were enriched for genes activated in response to wounding and affect the extracellular matrix including several markers such as SPP1, MMP9 and ITGB2. Conclusion: The proposed approach allowed us to identify clusters of genes that are non-randomly associated with the disease. Furthe

关键词： Co-expression network Differential network analysis Duchenne muscular dystrophy Preservation statistics Skeletal muscle

来源：评论

学校读者我要写书评

暂无评论

AI solutions for drafting in Magic: The gathering

arXiv

引用

arXiv 2020年

作者： Ward, Henry N. Brooks, Daniel J. Troha, Dan Mills, Bobby Khakhalin, Arseny S. Bioinformatics and Computational Biology Graduate Program University of Minnesota MinneapolisMN United States *** San DiegoCA United States Biology Program Bard College Annandale-on-HudsonNY United States

Drafting in Magic the Gathering is a sub-game within a larger trading card game, where several players progressively build decks by picking cards from a common pool. Drafting poses an interesting problem for game and AI research due to its large search space, mechanical complexity, multiplayer nature, and hidden information. Despite this, drafting remains understudied, in part due to a lack of high-quality, public datasets. To rectify this problem, we present a dataset of over 100,000 simulated, anonymized human drafts collected from ***. We also propose four diverse strategies for drafting agents, including a primitive heuristic agent, an expert-tuned complex heuristic agent, a Naive Bayes agent, and a deep neural network agent. We benchmark their ability to emulate human drafting, and show that the deep neural network agent outperforms other agents, while the Naive Bayes and expert-tuned agents outperform simple heuristics. We analyze the accuracy of AI agents across the timeline of a draft, and describe unique strengths and weaknesses for each approach. This work helps to identify next steps in the creation of humanlike drafting agents, and can serve as a benchmark for the next generation of drafting bots. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Interoperability of Digital Twin in the Maintenance phase of Lifecycle

Enhancing Interoperability of Digital Twin in the Maintenanc...

引用

International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Dedy Ariansyah Bens Pardamean Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

In today's digitalization era, asset management has evolved following the rapid adoption of information systems and digital technologies. In the maintenance phase of the asset, Digital Twin (DT) emerges as an effective approach to improve failure recognition and structural damage prediction through DT-driven Prognostic and Health Monitoring (PHM). As a result, DT allows asset manager to proactively plan maintenance strategy to improve the availability of the assets. However, to reduce asset downtime in a cost-effective and time efficient manner, DT for maintenance phase needs to be integrated with other software systems that manage the provision of maintenance service and spare part such as computerized maintenance management system (CMMS) or Enterprise Resource Planning (ERP). Nevertheless, many of the developed DT in PHM were not equipped with semantic data model and hence, lacks interoperability with other systems (e.g., other DT or non-DT system) and limits the effective use of the DT. To qualify as an effective DT, it needs a semantical-enabled interface to connect with data and models from various domain to enable DT-driven decision-making. This paper leverages a rule-based ontology reasoning model to enhance DT interoperability with other maintenance software systems. The results depicts that the developed model paves a way for an effective use of DT-driven system in asset maintenance management.

关键词： Ontologies Software systems Data models Enterprise resource planning Digital twins Prognostics and health management Interoperability

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：