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Analyzing important factors in e-commerce for sustainable industry

IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Yulius Lie Kartika Purwandari Bharuno Mahesworo Reza Rahutomo Anzaludin Samsinga Perbangsa Hery Harjono Muljo Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Accounting Information Systems Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The rapid growth of e-commerce for sustainable industry in Indonesia leads to competition for customer acquisition. Instead of gaining loyalty, customers are trapped in a price war and take advantage to compare and choose the one with the best offer. This study aims to analyze important factors of an online retailer to determine customer loyalty in the productive age group. This study contributes to design a questionnaire that can be utilized in analyzing customer loyalty to a retailer in e-commerce (popularly known as the term e-tailer). The questionnaire can be distributed through the Internet to gather demography, population, and e-loyalty factors. Questionnaire design is proposed containing three parts: Part A – Demography, Part B – Targeted Population, and Part C - e-Loyalty Factors. While Part A records basic information and Part B validates, Part C records respondent's assessment to an e - tailer. Therefore, the outcome delivers a data engineering method to help e-tailers determine important factors to gain customer loyalty.

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data annotation system for intelligent energy conservator in smart building

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IOP Conference Series: Earth and Environmental science 2020年第1期426卷

作者： Bens Pardamean Tjeng Wawan Cenggoro Bloomest Jansen Chandra Reza Rahutomo Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 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 Computer Science Department College of Letters and Science University of Wisconsin 53706 Madison WI USA

The concept of smart building includes the optimization of energy usage in a building. One of the possible solutions for this is to adaptively adjust appliances utilization according to activity level in the building. Thus, an intelligent activity estimation system needs to be developed. However, massive annotated dataset is necessary to train the system. Therefore, we propose a system that enables rapid data annotation to collect the massive dataset. With the proposed system, an image can be annotated within 4.8 seconds in average.

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Author Correction: Predicting the efficiency of prime editing guide RNAs in human cells

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Nature biotechnology 2024年第3期42卷 529页

作者： Hui Kwon Kim Goosang Yu Jinman Park Seonwoo Min Sungtae Lee Sungroh Yoon Hyongbum Henry Kim Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. Electrical and Computer Engineering Seoul National University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Graduate School of Data Science Seoul National University Seoul Republic of Korea. Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac. Severance Biomedical Science Institute Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of NanoScience and Technology Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac.

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Biclustering Method to Capture the Spatial Pattern and to Identify the Causes of Social Vulnerability in Indonesia: A New Recommendation for Disaster Mitigation Policy

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Procedia computer science 2019年 157卷 31-37页

作者： Puspita Anggraini Kaban Robert Kurniawan Rezzy Eko Caraka Bens Pardamean Budi Yuniarto Sukim Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia College of Informatics Chaoyang University of Technology Taichung City 41349 Taiwan (R.O.C.) Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Geographically, Indonesia is a meeting point of three continental plates. Scilicet, the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. Therefore, Indonesia is part of the infamous volcanic zone called the ”Ring of Fire” and one of the areas prone to natural disasters such as volcanic eruptions, earthquakes, tsunamis, floods, and landslides. This study aims to capture the spatial pattern and identify the causes of social vulnerability in the districts/cities in Indonesia using the biclustering method. The data is extracted from the Indonesian National Socio-Economic Survey (SUSENAS) by BPS-Statistics in 2014. The biclustering result indicates that each district/city has its own social vulnerability characteristics and shows that the vulnerable aspects of each district/city are different. The adjacent observations tend to have social vulenrability characteristics. The results of this study can be used as a reference for national disaster mitigation policy in Indonesia.

关键词： biclustering disaster mitigation social vulnerability SoVI spatial pattern

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Handling Missing Values in Multivariate Time Series Classification

Handling Missing Values in Multivariate Time Series Classifi...

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2018 IEEE MIT Undergraduate Research Technology Conference, URTC 2018

作者： Friend, Julia Hauck, Alec Kurada, Sruthi Hartvigsen, Thomas Sen, Cansu Rundensteiner, Elke A. Oberlin College Department of Computer Science United States Valparaiso University United States Advanced Math and Science Academy Charter School Marlborough United Kingdom Data Science Program Worcester Polytechnic Institute Worcester Polytechnic Institute Computer Science Department United States

ISBN: (纸本)9781538693742

Multivariate time series data are littered with missing values across many domains. Often the patterns of missing data points reflect conscious decisions by the data collector, and thus may contain correlations that can be used to more accurately classify the data. Previous works propose machine learning models that are capable of detecting these correlations in order to achieve highly accurate results. We review the literature and identify a rich variety of strategies along the machine learning pipeline for handling missing values, including forward imputation, appending additional missing indicators, and others. We also study different deep learning methods for handling time series data, namely GRU and LSTM cells. We then design an experimental study for investigating the relative effectiveness of different state-of-the-art methods for handling missing values to more fully comprehend the intricacies of missing data. For the experimental evaluation study, we utilize MIMIC-III, a publicly available critical care dataset composed of Electronic Health Records (EHR) for over 58,000 hospital admissions collected at Beth Israel Deaconess Medical Center from 2001 to 2012. In particular, our experimental study compares variety of missingness-aware machine learning methods for predicting patient mortality, a benchmark task for this database. We validate in general that extracting information from missing values indeed improves predictive accuracy, and we observe that GRU and LSTM cells perform comparably with the forward imputation method further impacting the resulting accuracy for both models. © 2018 IEEE.

关键词： data handling

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Reconstruction of Agents’ Corrupted Trajectories of Collective Motion Using Low-rank Matrix Completion

Reconstruction of Agents’ Corrupted Trajectories of Collect...

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IEEE International Conference on Big data

作者： Kelum Gajamannage Randy Paffenroth Department of Mathematics and Statistics Texas A&M University-Corpus Christi Corpus Christi USA Department of Mathematical Sciences Department of Computer Science and Data Science Program Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Tracking of trajectories of mutually interacted collectively moving agents such as fish, birds, insects, and even humans is an active field in computer vision. However, the trajectories produced by multi-object tracking methods might consist of unconstructed segments of trajectories due to the natural phenomena such as occlusion, change of illumination, etc., which require robust tracking methods. Some tracking methods employ computationally expensive approximation schemes to connect these segments. In this work, we utilize mutual interactions and dependencies between the agents to reconstruct the missing segments of the trajectories. Due to these interactions, the coordinate matrix representing the particles' trajectories of collective motion is often low-rank. Thus, we utilize a low-rank matrix completion technique to reconstruct incomplete trajectories. We apply this approach for two representative self-propelled particle swarms, simulated by the classic Vicsek model, that imitate two real-life collective motion scenarios and use low-rank approximations to analyze their incomplete trajectories.

关键词： Trajectory Motion segmentation Particle swarm optimization Tracking Biological system modeling Birds Manifolds

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Author Correction: Functional genomics data: privacy risk assessment and technological mitigation

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Nature reviews. Genetics 2022年第4期23卷 259页

作者： Gamze Gürsoy Tianxiao Li Susanna Liu Eric Ni Charlotte M Brannon Mark B Gerstein Computational Biology and Bioinformatics Program Yale University New Haven CT USA. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. Molecular Cellular and Developmental Biology Yale University New Haven CT USA. Statistics and Data Science Yale University New Haven CT USA. Department of Biology Stanford University Stanford CA USA. Computational Biology and Bioinformatics Program Yale University New Haven CT USA. mark@***. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. mark@***. Statistics and Data Science Yale University New Haven CT USA. mark@***. Computer Science Yale University New Haven CT USA. mark@***.

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An overview of facial micro-expression analysis: data, methodology and challenge

arXiv

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arXiv 2020年

作者： Xie, Hong-Xia Lo, Ling Shuai, Hong-Han Cheng, Wen-Huang The Institute of of Electronics National Chiao Tung Univresity Hsinchu300 Taiwan The Department of Electrical and Computer Engineering National Chiao Tung Univresity Hsinchu300 Taiwan The Institute of Electronics National Chiao Tung Univresity Hsinchu300 Taiwan The Artificial Intelligence and Data Science Program National Chung Hsing University Taichung400 Taiwan

Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g. police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Health Assistant Wearable-Based data science System Model: A Pilot Study 3

Health Assistant Wearable-Based Data Science System Model: A...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Budiarto, Arif Febriana, Trisna Suparyanto, Teddy Caraka, Rezzy Eko Pardamean, Bens Computer Science Department Bina Nusantara University School of Computer Science Jakarta11480 Indonesia Information Systems Department Bina Nusantara University School of Information Systems Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Indonesia Faculty of Science National University of Malaysia School of Mathematical Science Bangi43000 Malaysia Computer Science Department Bina Nusantara University BINUS Graduate Program-Master of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

The rapid development of Information and Communication Technology (ICT) has an impact on the innovation of new technology that gives benefits for health. One of its latest development is wearable device technology. Nowadays, the growth of this technology market is very positive. But unfortunately the growth of this technology is not followed by the availability of supporting applications that provide maximum benefits for users by utilizing personal health and activity data. This paper is intended to provide a report of our initial research in developing a health assistant solution that utilizes wearable device generated data. The whole research process is executed using Design Thinking approach that put the users as the main consideration. This initial research is started by designing a field research that can potentially gathered users' data generated from wearable device. A series of statistical and machine learning techniques are presented to analyze time series data to create a model. Finally, to implement the model, a mobile application prototype is also proposed. The outcomes from this initial research will be used to develop a fully working solution. © 2018 IEEE.

关键词： Learning systems

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