检索结果-内蒙古大学图书馆

6th International Conference on Interactive Digital Media, ICIDM 2020

作者： Zamahsari, Gamal Kusuma Putikadyanto, Agus Purnomo Ahmad Maulana, Fairuz Iqbal Bina Nusantara University Faculty of Economics and Communication Communication Science Department Jakarta11480 Indonesia Institute of Islamic Religion Madura Indonesian Language Education Study Program Pamekasan69371 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728149288

This study examines the efficiency of summative assessment in schools utilizing the *** assessment platform. The objectives of this study were (1) to describe staff efficiency on summative assessment using *** in schools, and (2) to describe the time efficiency on summative assessment using *** in schools. This study uses a qualitative design. The location of this research is Junior High School 3 Malang. data were collected through observation, document study, and interviews. Interviews were conducted with the vice-principal of the curriculum field. Investigation of validity data used triangulation, with additional interviews to the curriculum team and teachers at the school. Summative assessments using the *** platform show the more efficient in terms of staff's work as much as 17% compared to conventional summative assessment and a time efficiency of 44% compared to conventional assessments. The use of technology saves time from the planning, processing, and reporting stages of the assessment. The greatest efficiency is in the summative assessment processing stage because the costs of correcting or scanning student work, changing grades, and analyzing question items are not done manually. The use of technology with the *** platform for summative assessment in schools can be an alternative solution to promote teacher work. © 2020 IEEE.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Deep neural networks and weighted word embeddings for sentiment analysis of drug product reviews

引用

Procedia computer science 2023年 216卷 664-671页

作者： Derwin Suhartono Kartika Purwandari Nicholaus Hendrik Jeremy Samuel Philip Panji Arisaputra Ivan Halim Parmonangan Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

One of the major tasks of natural language processing is sentiment analysis. The web is a source of unstructured and rich informa-tion with thousands of opinions and reviews. Individuals, businesses, and governments can all benefit from recognizing sentiment. As part of this study, we propose a deep learning-based approach for sentiment analysis on drug product review data obtained from the UCI machine learning repository. As an alternative to deep learning models, this architecture integrates glove word embedding with convolutional neural networks (CNN). Word2vec and GloVe word embedding schemes have been evaluated empirically for their predictive performance in CNN architectures. Based on a comparison of the deep learning architecture with RoBERTa, itcan be seen that BERT architecture outperforms both of them in training and validation. However, CNN models using Glove word embedding provided superior results in testing.

关键词： transformers machine learning sentiment analysis weighted word embeddings

来源：评论

学校读者我要写书评

暂无评论

Social Network Analysis Algorithms, Techniques and Methods

Social Network Analysis Algorithms, Techniques and Methods

引用

2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Sandra, Lidia Trisetyarso, Agung Ramadhan, Arief Abdurachnan, Edi Lumbangaol, Ford Isa, Sani Muhamad Krida Wacana Christian University Psychology Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia Trisakti Institute of Trasportation and Logistic Trisakti University Jakarta Indonesia

ISBN: (纸本)9781665499699

Understanding how social structures with the use of a network have been an active field of study for academics in the past five years alone. The need to properly comprehends how Social Network Analysis (SNA) is being studied grows more and more in recent years. In this article, we propose a Systematic Literature Review (SLR) to the SNA to see how the algorithms, techniques, and methods are used also discuss their findings. We select thirty-one research studies on SNA. We found different algorithms and techniques that are being used. It is found that the selected research could be categorized into five different main topics which is academic, health, social media, communication, and technology. From all of the research paper discussed, it is also found that many algorithms and techniques are being used to enhanced the SNA, most of them are being machine learning algorithm such as Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression, K Nearest Neighbor, and Whale Optimization Algorithm. While the common features of the datasets used in the research comes as different arrays of user information from social media platforms, Tweets and posts from multiple platforms, also a photographic input such as self-images, portraits, and context related pictures. This article will serve as a single reference for future researchers to the discovery of the latest SNA findings. © 2021 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Logistic Regression and Decision Tree Models for Lung Cancer Prediction Using Survey data

Evaluation of Logistic Regression and Decision Tree Models f...

引用

2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Narayana, K.E. Jayashree, K. ThavaVinu, A. Sindhuja, M. Jaeyalakshmi, M. Rajalakshmi Engineering College Department of Information Technology Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Prathyusha Engineering College Department of Information Technology Chennai India Acharya University Department of Computer Science and Engineering Karakul Uzbekistan Rajalakshmi Engineering College Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798331522988

Lung cancer is one of the most deadly and ubiquitous forms of cancer globally. Early detection can make a significant difference in survival rates, prognosis, etc. Background The present study compares the performance of logistic regression and decision trees using a survey-based dataset for predicting lung cancer. This dataset has a total of 284 cases with 16 features;behavioral patterns, symptomatic data, and demographic details. Logistic regression is a binary classification algorithm that uses input features to model the probability of lung cancer presence given these input features using for example a sigmoid function. Using Decision Trees can identify significant fields in a dataset while reducing Gini Impurity for models that categorize patients based on feature values. The performance of both models is evaluated according to accuracy, F1-score, precision, and recall measures. From the preliminary results, it looks like the Decision Tree works a little better than the logistic regression model which gets an accuracy of 94%, but this one does with more accuracy (97%). This result emphasizes the inclusion of logistic regression within this context, as it may be able to predict lung cancer with greater accuracy. Additionally, to ensure the generalization and robustness of the model, further investigation and trials will focus on optimizing performance through ensemble approaches, hyperparameter tuning, and cross-validation. This comparative analysis demonstrates the effectiveness of machine learning techniques in developing prediction models for early lung cancer detection, an important step toward a quick and appropriate intervention. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Benefits and Threats of Telemedical Application in Psychiatric Services 1

Benefits and Threats of Telemedical Application in Psychiatr...

引用

1st International Conference on Technology, Engineering, and Computing Applications, ICTECA 2023

作者： Martaningga, Farra Mantoro, Teddy Andoko, Djoko Postgraduate Program of Medical Intelligence National Intelligence College Bogor Indonesia Sampoerna University Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350360554

Health development is one part of national development. Telemedical is an innovation in engineering and computing application technology healthcare in the era of society 5.0 is needed as a supporting tool. The purpose of this paper is to explain the benefits and threats of using telemedicine in the field of psychiatry in the care system. This study uses a qualitative descriptive method, data collection by literature study, which is a method that explains a phenomenon regarding the benefits and threats of telemedicine in psychiatric services. Telepsychiatry is a health service carried out by mental medicine specialists using information and communication technology to diagnose, treat and estimate the patient's health condition in accordance with their ability and authority as evidenced by the validity of the Registration Certificate (STR) and Practice Permit (SIP) while maintaining service quality and patient safety. Telepsychiatry is carried out by mental medicine specialists with other doctors, or mental medicine specialists with patients in a Health Service Facility (Fasyankes), or between Fasyankes. As a result of the dialogue, Telepsychiatry has demonstrated significant cost-effectiveness and efficiency as a viable option to conventional psychiatric services, increasing accessibility for patients seeking providers. Innovative advances in the field of mental healthcare technology solutions have the potential to provide substantial benefits to recipients and stakeholders. Threats to telemedicine, including telepsychiatry, include misuse of patients' personal data by employees or hacking by cybercriminals for purposes that jeopardise the security of patients and the country. Special attention should be paid to the problem of misuse and hacking of patient data in telemedicine by individuals and hackers. © 2023 IEEE.

关键词： Telemedicine

来源：评论

学校读者我要写书评

暂无评论

An incremental approach for niching and building block detection via clustering

An incremental approach for niching and building block detec...

引用

7th International Conference on Intelligent Systems Design and Applications, ISDA'07

作者： Emmendorfer, Leonardo Ramos Pozo, Aurora Trinidad Ramirez Numerical Methods in Engineering PhD Program Federal University of Paraná Brazil Department of Computer Science Federal University of Paraná Brazil

ISBN: (纸本)0769529763

Diversity preservation has already been established as an important concern for evolutionary computation. Clustering techniques were, among others, successfully applied to this purpose. Another important aspect of the research on evolutionary computation is related to linkage learning - the detection of the problem structure avoiding disruption of building blocks when new individuals are generated. This paper presents a novel approach which is a new estimation of distribution algorithm (EDA) where clustering plays two roles: diversity preservation and linkage learning. Initial empirical investigations illustrate the behavior of the algorithm when solving two benchmark optimization problems. © 2007 IEEE.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Dynamics signature based anomaly detection

Dynamics signature based anomaly detection

引用

作者： Goenawan, Ivan Hendy Du, Zhihui Wu, Chao Sun, Yankui Wei, Jianyan Bader, David A. Department of Computer Science and Technology Tsinghua University Beijing China Department of Data Science New Jersey Institute of Technology NewarkNJ United States National Astronomical Observatories Chinese Academy of Sciences Beijing China

Identifying anomalies, especially weak anomalies in constantly changing targets, is more difficult than in stable targets. In this article, we borrow the dynamics metrics and propose the concept of dynamics signature (DS) in multi-dimensional feature space to efficiently distinguish the abnormal event from the normal behaviors of a variable star. The corresponding dynamics criterion is proposed to check whether a star's current state is an anomaly. Based on the proposed concept of DS, we develop a highly optimized DS algorithm that can automatically detect anomalies from millions of stars' high cadence sky survey data in real-time. Microlensing, which is a typical anomaly in astronomical observation, is used to evaluate the proposed DS algorithm. Two datasets, parameterized sinusoidal dataset containing 262,440 light curves and real variable stars based dataset containing 462,996 light curves are used to evaluate the practical performance of the proposed DS algorithm. Experimental results show that our DS algorithm is highly accurate, sensitive to detecting weak microlensing events at very early stages, and fast enough to process 176,000 stars in less than 1 s on a commodity computer. © 2021 John Wiley & Sons Ltd.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Neural Network Sliding-Mode Control with Actor-Critic for a Class of Robot Systems 10

Fuzzy Neural Network Sliding-Mode Control with Actor-Critic ...

引用

10th International Conference on Orange Technology, ICOT 2022

作者： Hung, Lon-Chen Wei, Chun-Cheng Chen, Hung-Jen Yang, Chih-Fu Cheng, Wei-Min Lunghwa University of Science and Technology Department of Electronic Engineering Taiwan School of Software and Big Data Changzhou College of Information Technology China Lunghwa University of Science and Technology Department of Computer Information and Network Engineering China

ISBN: (纸本)9781665454193

In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. The fuzzy RBF neural network sliding-mode and actor-critic method is handled to compensate the uncertainty and disturbance of system. The stability analysis is based on for the proposed adaptive and robust control method. The simulation results show the effectiveness under the uncertainties. © 2022 IEEE.

关键词： Sliding mode control

来源：评论

学校读者我要写书评

暂无评论

Performance Assessment of Classical Machine Learning Models Used for Entity Relationship Extraction on Online Philippine News Articles 6

Performance Assessment of Classical Machine Learning Models ...

引用

6th International Conference on Electrical, Control and Instrumentation Engineering, ICECIE 2024

作者： Ogbinar, Michel Onasis S. Ignacio, Michael Q. Penas, Reynaldo Ted L. Naval, Prospero C. University of the Philippines - Diliman Artificial Intelligence Program Quezon Philippines University of the Philippines - Diliman Department of Computer Science Quezon Philippines

ISBN: (纸本)9798350380040

Content from legitimate online news articles is used to determine relationships of objects or entities to enable a more efficient way of extracting information. It could also be used to establish ground truths on news stories or current events to provide a reference against the emergence of fake news articles. This study involved the entity relationship extraction in an online news source, the Philippine Daily Inquirer online site ***. Classical machine learning methods were designed to perform pairwise entity relationships via Semantic Web, and the performance of the methods was assessed and compared with one another in terms of training time and F1 scores. A 90-10 train-test split of data was used for each of the methods. A total of 12 machine learning methods were configured to accomplish the task of entity relationship extraction, and a total of 27 top entity relationships were obtained and ranked. The use of support vector machines (SVM) yielded the highest F1 score of 60.73% but took 592.54 seconds to complete the training. The fastest method to train was the multinomial naïve Bayes (MNB) at 0.63 seconds but only obtained an F1 score of 50.14%. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Evaluative Study of Integration of Clinic Management Information System with Pcare BPJS of health Using COBIT 4.1 Approach

Evaluative Study of Integration of Clinic Management Informa...

引用

2019 International Conference on computer science, Information Technology, and Electrical Engineering, ICOMITEE 2019

作者： Mulyadi, Ely Aryanto, Kadek Yota Ernanda Candiasa, I. Made Politeknik Negeri Jember Department of Information Technology Jember Indonesia Universitas Pendidikan Ganesha Department of Computer Science of Postgraduate Program Singaraja Indonesia

ISBN: (纸本)9781728134369

The aim purposed of this research is to evaluate clinical management information system called (E-CLINIC) which is integrated with the Primary Care (Pcare) application provided by Indonesia sosial healt insurance organization called BPJS Kesehatan to first level of health cares facility called as FKTP. This evaluation is intended to measure the maturity level on used of two integrated applications using COBIT 4.1 framework. The integration of the Clinic's management information system with PCare is considered to be the main resource that has strategic value to be able to manage information effectively and efficiently for the achievement of organizational goals. The results achieved have been running in accordance with the organization's business objectives, this can be seen from the level of maturity that reaches level 3 (Defined), which is a condition in which the organization has formal and written standard procedures that have been socialized to all levels of management and employees to be obeyed and run in daily activities. © 2019 IEEE.

关键词： Information management

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：