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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

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Multiple analyses indicate the specific association of NR1I3,C6 and TNN with low hip BMD risk

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Journal of Genetics and Genomics 2017年第6期44卷 327-330页

作者： Ying-Ying Han Lan-Juan Zhao Yong Lin Hao He Qing Tian Wei Zhu Hui Shen Xiang-Ding Chen Hong-Wen Deng Center of System Biomedical Sciences University of Shanghai for Science and Technology Center for Bioinformatics and Genomics Department of Global Biostatistics and Data ScienceSchool of Public Health and Tropical MedicineTulane University Laboratory of Molecular and Statistical Genetics and the Key Laboratory of Protein Chemistry and Developmental Biology of the Ministry of Education College of Life SciencesHunan Normal University

Low hip bone mineral density(BMD)is an important index for osteoporosis and is associated with hip fracture,which leads to more cases of disability and mortality than all other kinds of fractures(Kanis et al.,2007).BMD’s heritability is more than 60%(Arden et al.,1996).A number of candidate loci for BMD have been previously identified by Genome Wide Association Studies(GWAS)(Xiong et al.,2009;Karasik et al.,2010;Zhang et al.,2013).Nevertheless,many significant signals based on GWAS are

关键词： BMD ERE is in Multiple analyses indicate the specific association of NR1I3,C6 and TNN with low hip BMD risk of GWAS BRCA NR with

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Embedding Model Design for Producing Book Recommendation 4

Embedding Model Design for Producing Book Recommendation

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4th International Conference on Information Management and Technology, ICIMTech 2019

作者： Rahutomo, Reza Perbangsa, Anzaludin Samsinga Soeparno, Haryono Pardamean, Bens Bioinformatics Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Internet services often recommend contents to users in order to maintain the interaction. Recommendation system plays a major role to formulate and produce a series of recommendation based on users' behavior. Surprisingly, user-generated scoring or known as ratings are the main raw materials to learn the pattern of favorable contents of each user. As a part of collaborative filtering strategy, rating is considerable to be included in formulating recommended contents. In this research, the basic formulation to recommend books is discovered. The recommendation system has been tested on one random-picked user behavior and successfully has generated five recommended books by analyzing prior activities. The embedding model produced the recommended books with 59% of accuracy. This research is done to provide an insightful experience for developing content recommendation system in Binus University's corporate learning system. © 2019 IEEE.

关键词： Recommender systems

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Unique,Persistent,Resolvable:Identifiers as the Foundation of FAIR

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data Intelligence 2020年第1期2卷 30-39,302页

作者： Nick Juty Sarala M.Wimalaratne Stian Soiland-Reyes John Kunze Carole A.Goble Tim Clark Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK California Digital Library OaklandCalifornia 94612-2901USA Data Science Institute University of VirginiaCharlottesvilleVA 22903-1738USA

The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ***,globally unique identifiers,resolvable on the Web,and associated with a set of additional descriptive metadata,are foundational to FAIR *** we describe some basic principles and exemplars for their design,use and orchestration with other system elements to achieve FAIRness for digital research objects.

关键词： Identifiers metadata findability FAIR data data infrastructures

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Dysregulation of microRNA in cholangiocarcinoma identified through a meta-analysis of microRNA profiling

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World Journal of Gastroenterology 2020年第29期26卷 4356-4371页

作者： Somsak Likhitrattanapisal Supeecha Kumkate Pravech Ajawatanawong Kanokpan Wongprasert Rutaiwan Tohtong Tavan Janvilisri National Center for Genetic Engineering and Biotechnology Pathumthani 12120Thailand Department of Biology Faculty of ScienceMahidol UniversityBangkok 10400Thailand Division of Bioinformatics and Data Management for Research Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok 10700Thailand Department of Anatomy Faculty of ScienceMahidol UniversityBangkok 10400Thailand Department of Biochemistry Faculty of ScienceMahidol UniversityBangkok 10400Thailand

BACKGROUND In the past decades,the potential of microRNA(miRNA)in cancer diagnostics and prognostics has gained a lot of *** this study,a meta-analysis was conducted upon the pooled miRNA microarray data of cholangiocarcinoma(CCA).AIM To identify differentially expressed(DE)miRNAs and perform functional analyses in order to gain insights to understanding miRNA-target interactions involved in tumorigenesis pathways of *** Raw data from 8 CCA miRNA microarray datasets,consisting of 443 samples in total,were integrated and statistically analyzed to identify DE miRNAs via comparison of levels of miRNA expression between CCA and normal bile duct samples using t-tests(P<0.001).The 10-fold cross validation was performed in order to increase the robustness of the t-test *** data showed 70 up-regulated and 48 down-regulated miRNAs in CCA. GeneOntology and pathway enrichment analyses revealed that mRNA targets of DEmiRNAs were significantly involved in several biological processes. The mostprominent dysregulated pathways included phosphatidylinositol-3 kinases/Akt,mitogen-activated protein kinase and Ras signaling *** miRNAs found in our meta-analysis revealed dysregulation in major cancerpathways involved in the development of CCA. These results indicated thenecessity of understanding the miRNA-target interactions and the significance ofdysregulated miRNAs in terms of diagnostics and prognostics of cancers.

关键词： Cholangiocarcinoma Microarray MicroRNA Meta-analysis

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Comparative Analysis of CNN Models for Multiclass Detection on Colon Disease 9

Comparative Analysis of CNN Models for Multiclass Detection ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Isnan, Mahmud Pardamean, Bens Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Gastrointestinal diseases are significant health concerns that mostly affect the digestive and biliary tracts. These can only be observed internally through endoscopy, or a modern approach named Wireless Capsule Endoscopy. Though the approach has proven to work, the process relies heavily on medical experts, which is prone to error when given a considerably large load. The use of deep learning in detecting diseases with images has been proven to work in various cases and can be implemented for gastrointestinal diseases. This study compared four deep learning models based on Convolutional Neural Network architectures consisting of the VGG16, ResNet152V2, InceptionV3, and Xception for classifying colon disease images. The experiment showed that the ResNet152V2 performed the best, compared to the other three with a testing accuracy score of 0.9837. On the other hand, the VGG16 had the lowest performance with around accuracy score of 0.89 while the Xception and InceptionV3models yielded similar accuracy scores of 0.9587 and 0.9549, respectively. The results highlight the effectiveness of ResNet152V2 in handling the complexity of colon disease detection © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Multiple Choice Question Generation: A Systematic Literature Review 9

Automatic Multiple Choice Question Generation: A Systematic ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Nugroho, Kuncahyo Setyo Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Despite their drawbacks, multiple-choice questions (MCQ) have been widely used to assess the students' understanding of lectures through examinations. The development of automatic MCQ generation is beneficial, especially for educators. As a starting point for further development, a Systematic Literature Review (SLR) is conducted to uncover current trends, future challenges, and opportunities in automatic MCQ generation. Previously, an SLR was conducted, but it lacks coverage of the utilization of transformer-based models. This SLR covers the development of automatic MCQ generation using either traditional or advanced approaches such as Transformers. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used to gather the data from Scopus, IEEE Xplore, SpringerLink, arXiv, and Semantic Scholar. The included articles must be open-access computer science conference papers or journal articles and written in English less than five years ago. Four independent reviewers analyzed the research workflow, evaluation metric, and dataset used in each study. There are 18 included studies, where 17% (n = 3) studies are from 2024, 33% (n = 6) studies are from 2023, 22% (n = 4) studies are from 2022, 11% (n = 2) studies are from 2021, and 17% (n = 3) studies are from 2020. There are 33% (n = 6) of the studies used the traditional feature-based engineering approach, 39% (n = 7) of the studies used the Transformer-based model fine-tuning approach, and the remaining used novel approaches. The study found that BERT variants are the most utilized Transformer-based model in automatic MCQ. The research notes some challenges, but also open various opportunities for further research, including Large Language Model (LLM) utilization for automatic MCQ generation, the utilization BERT-based models for standardized machine-learned evaluation metrics, and the initiative for the creation of an MCQ dataset benchmark. © 2024 IEEE.

关键词： Semantics

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Automatic question generation for bahasa indonesia examination using copynet 9th

Automatic question generation for bahasa indonesia examinati...

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

作者： Henry, Matthew Martianus Elwirehardja, Gregorius Natanael Pardamean, Bens 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 Bina Nusantara University Jakarta11480 Indonesia

In educational institutions, an educator is responsible for assessing the student's knowledge grasp through examination. Creating exam questions, even the low-level factoid questions, is time-consuming, especially for inexperienced educators. Therefore, this study aims to create a sequence-to-sequence model using CopyNet by exploiting its copying mechanism advantage to automatically generate Bahasa Indonesia factoid questions to ease the educator's burden. Indonesian records in the TyDi QA dataset are used as the model input. GRU and Bi-GRU are employed as the CopyNet encoder, while LSTM is used as the CopyNet decoder. The model that utilizes GRU as the encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.28, 0.19, 0.14, 0.1, and 0.32, respectively. Bi-GRU utilization as the model encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.26, 0.17, 0.12, 0.09, and 0.30, respectively. Models using either encoder still achieve low scores. However, compared with the previous work, the result is still on par regarding the BLEU score. Further examination found that the generated questions do not adhere to semantic and syntactical correctness. Adding more records to the dataset and utilizing a more advanced architecture like CopyBERT are encouraged to improve the model performance in future work. Despite the result, this study has shown that CopyNet, primarily designed for text summarization or single-turn dialogue, can be tailored for factoid question generation. © 2024 The Authors.

关键词： Semantics

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Parallel genome-wide analysis with central and graphic processing units

Parallel genome-wide analysis with central and graphic proce...

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International Conference on Computer and Communications (ICCC)

作者： Muhamad Fitra Kacamarga James W. Baurley Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia

The Indonesia Colorectal Cancer Consortium (IC3), the first cancer biobank repository in Indonesia, is faced with computational challenges in analyzing large quantities of genetic and phenotypic data. To overcome this challenge, we explore and compare performance of two parallel computing platforms that use central and graphic processing units. We present the design and implementation of a genome-wide association analysis using the MapReduce and Compute Unified Device Architecture (CUDA) frameworks and evaluate performance (speedup) using simulated case/control status on 1000 Genomes, Phase 3, chromosome 22 data (1,103,547 Single Nucleotide Polymorphisms). We demonstrated speedup on a server with Intel Xeon E5-2620 (6 cores) and NVIDIA Tesla K20 over sequential processing.

关键词： Graphics processing units Genomics Instruction sets Cancer Kernel bioinformatics Computer architecture

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A review: data pre-processing techniques used for diabetes prediction 9th

A review: Data pre-processing techniques used for diabetes p...

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

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Cmputer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification. © 2024 The Authors.

关键词： Adversarial machine learning

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