检索结果-内蒙古大学图书馆

7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Daniel Cenggoro, Tjeng Wawan 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 Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 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. © 2022 Elsevier B.V.. All rights reserved.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Real-time monitoring system for sudden cardiac death based on container orchestration and Binary Serialization

Real-time monitoring system for sudden cardiac death based o...

引用

2021 International Symposium on Electronics and Smart Devices, ISESD 2021

作者： Surantha, Nico Wincent Muhamad Isa, Sani BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9781665441469

Sudden Cardiac Death (SCD) is happening because the heart stops pumping the blood to the brain, lungs, et cetera, which will cause death within an hour. With this high risk and low survivability rate, real-time Internet of Things (IoT) monitoring systems have become a solution to tackle this problem. The system needs to be accessible at any time and condition by multiple users who bring up the needs of the availability and scalability of the system. Some research had been conducted on IoT monitoring system but still the result of availability and scalability not acceptable for a real-life scenario. The purpose of this research is to propose an architecture for a real-time IoT monitoring system for sudden cardiac death by using Kubernetes as the container orchestration and ProtocolBuffers as the Binary Serialization. The result shows us applying Kubernetes and ProtocolBuffers gives a good result with 100% successfully stored data and 50.5s end-to-end Execution time for 50000 requests which reflect a high availability and fast system. © 2021 IEEE.

关键词： Containers

来源：评论

学校读者我要写书评

暂无评论

Leveraging Social Learning for Improved Cybersecurity Maturity: A Case Study Using the NIST Framework

Leveraging Social Learning for Improved Cybersecurity Maturi...

引用

Information Technology and Computing (ICITCOM), International Conference on

作者： Marcel Meyliana Harco Leslie Hendric Spits Warnars Tirta Nugraha Mursitama Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department Krida Wacana Christian University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Doctor of Research in Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

This research shows that social learning can be used to increase an organization's cybersecurity maturity level. Using a literature study and case study approach. Literature studies are used to identify social learning's key success factors (KSF) and challenges in general. Then, the case study was performed in the context of assessing the maturity level of information security using the NIST Cybersecurity framework at ABC Software House, a small-scale software development company in Indonesia. Assessment activities are performed through FGDs which then also identify the most relevant social learning’ KSF to increase the organization's information security maturity level. Next, referring to the selected KSF, a process of identifying and mapping social learning’ challenges was carried out. The integration of social learning can help organizations gain better focus and understanding in nontechnical contexts to increase their maturity level. This research highlights that integrating social learning with the NIST cybersecurity framework can provide organizations with better focus and perspective for formulating non-technical solutions. This study also enriches the existing literature on social learning in cybersecurity and offers practical insights for organizations looking to improve their cybersecurity resilience.

关键词： Training Collaboration Information security NIST Software computer security Information technology Monitoring Software development management Resilience

来源：评论

学校读者我要写书评

暂无评论

Multilabel Machine Learning System for Predicting Gender and Age Group from Human Gait 2

Multilabel Machine Learning System for Predicting Gender and...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Qomariyah, Nunung Nurul Nurul Fajar, Ahmad Yono, Vincent Kazakov, Dimitar Bina Nusantara University School of Computing and Creative Arts Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Information System Management Information Systems Management Department Jakarta11480 Indonesia University of York Computer Science Department YO10 5GH United Kingdom

ISBN: (纸本)9798350360523

This paper explores the development of a multilabel machine learning system for predicting both gender and age from human gait patterns. Gait analysis, a non-intrusive method of identifying subtle nuances in human movement, has proven to be a rich source of information related to demographic characteristics. The research extends beyond traditional single-label classification approaches, adopting a multilabel framework to simultaneously predict gender and age *** study evaluates various multilabel machine learning algorithms, with the Random k-labeLsets (RAKEL) algorithm demonstrating superior performance in predicting gender and age labels from human gait datasets. The accuracy of the algorithm can reach up to 87%. We also compared the multilabel approach with several multiclass algorithms such as Decision Tree, Random Forest, Gradient Boosting, K-Neighbors and XGBoost. However, when we also considering the training time, Classifier Chain algorithm showed the best trade off with the accuracy of 86% and the training time is twice faster than the RAKEL algorithm. © 2024 IEEE.

关键词： Gait analysis

来源：评论

学校读者我要写书评

暂无评论

A review: Data pre-processing techniques used for diabetes prediction

引用

Procedia computer science 2024年 245卷 667-676页

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cmputer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 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.

关键词： Diabetes Classification Data Pre-Processing Machine Learning Imbalanced Dataset

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Methods for Predicting the Necessity of Caesareans Section of Childbirth 5

Machine Learning Methods for Predicting the Necessity of Cae...

引用

5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Sunge, Aswan Supriyadi Heryadi, Yaya Abdurachman, Edi Kartowisatro, Iman H. Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty Of Engineering Computer Science Departement - BINUS Graduate Program Computer Science Computer Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

In the process of delivery usually the baby comes out of the vagina but under some circumstances a cesarean section is performed. Caesarean section, on the one hand can have short-term and long-term effects for the mother but on the other hand it is a life-saving procedure for both mother and child. The purpose of this study is to predict whether or not a caesarean section is necessary for a pregnant mother using a machine learning classifier model and some features from medical records as input data. In this study, some machine models were explored including Logistic Regression, K-Nearest Neighbor, Random Forest, Decision Tree, Gradient Boosting and Support Vector Machine. The experiment found that Logistic Regression model achieved the highest performance as measured by average accuracy 95%, average precision 86%, average recall 95%, and average F1 86%. © 2021 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Data Mining for the Global Multiplex Weekly Average Income Analysis

引用

Procedia computer science 2023年 219卷 52-59页

作者： Nicholas Dominic Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The box office (BO) income had significantly declined up to 80% in 2020, as the COVID-19 pandemic emerged. To minimize further financial risks, multiplex (multiple cinema complexes) owners need to analyze their potential income for each movie, each week. Therefore, we developed a proper data mining strategy that allows multiplex owners to analyze and discover insights on how successfully produced movies could be. The methodology comprises (1) data loading and exploration, (2) data cleaning, (3) data selection, integration, and transformation using Pentaho, (4) data mining in which the results were stored in the MySQL database, and (5) pattern evaluation and presentation using Qlik Sense as the Business Intelligence (BI) dashboard. Based on our data mining methodology, we revealed that drama, comedy, action, and thriller are favorite genres. We also found that DreamWorks Animation and Pixar Animation Studios are both the most popular production houses, even Apatow Productions and Escape Artists still have the biggest revenue on average.

关键词： movie multiplex income data mining Business Intelligence dashboard

来源：评论

学校读者我要写书评

暂无评论

E-Government Interoperability: Provincial-Level Architecture Model to Enable Fast Healthcare Interoperability Resources (FHIR)

E-Government Interoperability: Provincial-Level Architecture...

引用

International Conference on Information Management and Technology (ICIMTech)

作者： Diana Wijayanti Satriawansyah Urbaya Taufiq Sitompul Verry Adrian Department of Information Systems BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Country Health Information Systems and Data Use (CHISU) Program USAID Indonesia Jakarta Indonesia Department of Informatics University of Oslo Oslo Norway Computer Science Department Binus Graduate Program – Doctor Computer Science Bina Nusantara University Jakarta Indonesia Center of Data and Information Technology Jakarta Provincial Health Office Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

The process of using ICT to provide services to the public is known as the Indonesian e-Government system, or Sistem Pemerintahan Berbasis Elektronik (SPBE). The e-Government initiative in Jakarta Provincial Health Office involves enhancing collaboration among public health entities for efficient data exchange and streamlined processes, especially between the Provincial and District Health Offices, public hospitals, government clinics, and primary health care centers (Puskesmas). Achieving interoperability requires standardized protocols and a well-defined architectural model to integrate data seamlessly. This study presents a provincial-level architectural model focused on improving electronic health records interoperability, aiming to promote the adoption of the national Fast Healthcare Interoperability Resources (FHIR) health information exchange platform and enhance the integrity of health data in Jakarta. The study methodology involves conducting literature reviews, observations, and discussions with representatives from healthcare facilities to develop the e-Government architecture model and prototype of the infrastructure layer aiming to facilitate the interoperability of Electronic Health Records (EHRs) across 93 healthcare facilities, all of which are part of the SPBE users.

关键词： Prototypes Medical services Data models Security Public healthcare Electronic medical records Interoperability Information exchange Standards Electronic government

来源：评论

学校读者我要写书评

暂无评论

Deaf Helper Mobile Application for Interaction of Hearing Disorders Communities 2

Deaf Helper Mobile Application for Interaction of Hearing Di...

引用

2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Setyawan, Adrian Naphan, Gilbert Xervaxius Dynata, Kevin Friry, Jovano Edmund Warnars, Harco Leslie Hendric Spits Bina Nusantara University School of Information System Information Systems Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

People with hearing loss in this world have not received much serious attention from the authorities. This makes these sufferers confused in choosing learning media to interact with and isolated from their social environment. This application was created to help people with hearing loss to be noticed and understood from the way they communicate using sign language through the mobile application called Assistant for the Deaf, which has many features such as registration, interactive videos, sign language translator, forums, customer service, library, information, history, events, donations, and shops. The application is designed using use case diagrams and class diagrams modeling the database, and the implementation used Android Studio and MySQL database. © 2022 IEEE.

关键词： Audition

来源：评论

学校读者我要写书评

暂无评论

Analysis of ensemble majority voting approach for acute lymphoblastic leukemia detection using svm trained on white blood cell abnormalities in images 9th

Analysis of ensemble majority voting approach for acute lymp...

引用

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

作者： Januardo, Bryan Putradinata, Harley Moniaga, Jurike V. Nabiilah, Ghinaa Zain Computer Science Department Binus Graduate Program Jl. Raya Kb. Jeruk No.27 RT.1/RW.9 Kemanggisan Kec. Palmerah Kota Jakarta Barat Daerah Khusus Ibukota Jakarta11530 Indonesia

Leukemia is a cancer that attacks and infects white blood cells which can hinder the capability for someone with leukemia to fight infections, which may cause severe complications or even death. While Acute Lymphoblastic Leukemia is a certain type of leukemia that is the most prevalent childhood cancer. Detecting this disease is a repetitive activity that can take a lot of time and resources, meanwhile Acute Lymphoblastic Leukemia has a fast growth rate. In this study, we will try to classify leukemia cancer using machine learning based on the images of white blood cells provided. This method could provide early diagnosis and reduce the burden on hematologist-oncologist by optimizing the resources that are available. This research will use the ensemble classifier concept by combining several SVM models like linear, polynomial, and RBF. That have been trained, then combined into one singular ensemble model. By combining these models, we hope to improve the classification performance by minimizing the drawbacks of using certain SVM kernels. The results of this classification obtained an accuracy performance of 70.01%. © 2024 The Authors.

关键词： Lung cancer

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：