检索结果-内蒙古大学图书馆

9th International Conference on information Management and technology, ICIMTech 2024

作者： Marcel Meyliana Warnars, Harco Leslie Hendric Spits Mursitama, Tirta Nugraha BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Business School Doctor of Research in Management Bina Nusantara University Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This research develops a conceptual model to understand the influence of digitalization on social learning, especially in the context of community empowerment. This model maps the relationship between factors that influence success, challenges faced, and variations in the use of digital platforms in social learning. By using a literature study approach, the research results show that effective communication, collaboration and empowerment are the main keys to the success of social learning in society. Key challenges identified include limited access to information, resource constraints, and digital literacy barriers, all of which require specific strategies to overcome. By examining the relationship between success factors, challenges, and use of digital platforms, this research provides new insights that support the development of more effective strategies for using digital technology to support social learning that leads to community empowerment. This research has great potential to increase people's access to learning opportunities and information, allowing them to be more active in the social learning process. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Trend Intelligent Tutoring System 2018-2022 : Systematic Literature Review 3

Trend Intelligent Tutoring System 2018-2022 : Systematic Lit...

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Suwita, Jaka Prabowo, Harjanto Hendric Spits Warnars, Harco Leslie Meyliana, Meyliana Binus Graduate Program Computer Science Bina Nusantara University Departement of Computer Science Jakarta11480 Indonesia Stmik Insan Pembangunan Department of Information System Tangerang15810 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350381931

ITS is a method of teaching that uses technology to give lessons without the need for an instructor to be there. A systematic literature review (SLR) was conducted in this research to identify the components of an intelligent tutoring system, and the results are presented. The method used in this literature review is the prism method. The steps in the prism method are slr protocol, search for literature, select literature, summarize evidence and disseminate results. The slr protocol stage, it consists of determining a research question, searching for scientific reference sources with the keyword intelligent tutoring system, then sorting out the appropriate articles and making final conclusions. This article gives a detailed look at how the intelligent tutoring system changed over time during the pandemic epoch. Our contribution is complete information on the components of ITS in times of epidemic at universities, with the goal of improving their learning and practical use of the technology. © 2024 IEEE.

关键词： Prisms

来源：评论

学校读者我要写书评

暂无评论

Optimizing Preacher Assignments Using AHC-PCA Algorithm in Kampar Regency 2

Optimizing Preacher Assignments Using AHC-PCA Algorithm in K...

引用

2nd IEEE International Conference on Electrical Engineering, computer and information technology, ICEECIT 2024

作者： Kurniawan, Rahmad Suhada, Nuraina Sukamto Melia, Tisha Zailani Risanto, Joko Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798331504373

Inefficiencies in assigning preachers for Islamic preaching activities often arise due to distant placements, leading to delays and logistical challenges. In Kampar Regency, geographical diversity intensifies this issue, resulting in suboptimal preacher placements. This study aims to optimize preacher assignments by considering distance and mosque profiles. The dataset, which includes 102 mosques and 148 preachers was obtained from the Indonesian Ulema Council in Kampar. The Agglomerative Hierarchical Clustering (AHC) algorithm was selected for its capacity to cluster data across multiple characteristics. Principal Component Analysis (PCA) was employed to reduce data dimensionality, thereby enhancing clustering outcomes. The optimization of the AHC-PCA algorithm generated an optimal clustering solution with a silhouette score of 0.625524 at three clusters. Furthermore, the elbow method also indicated that three clusters are optimal with a value of 390.234457, while the lowest Davies-Bouldin Index (DBI) score of 0.468573 was achieved with three clusters. Business Intelligence techniques visualized these clusters, providing a spatial distribution of preachers and mosques for detailed analysis. This study improves preacher assignments by reducing travel time and aligning preacher placements with mosque proximity and needs, thus enhancing the efficiency of Islamic preaching in Kampar Regency. © 2024 IEEE.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

An Improved Transfer Learning-Based Model with Data Augmentation for Brain Tumor Detection 6

An Improved Transfer Learning-Based Model with Data Augmenta...

引用

6th International Conference on Artificial Intelligence in information and Communication, ICAIIC 2024

作者： Prayogo, Rizal Dwi Hamid, Nur Nambo, Hidetaka Graduate School of Natural Science and Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Faculty of Transdisciplinary Sciences Kanazawa Japan

ISBN: (纸本)9798350344349

Current advances in deep learning have brought various breakthroughs in processing medical data. However, dealing with a limited number of medical datasets remains a challenge in deep learning and often leads to overfitting. To solve this research gap, here we show a new approach to improve the performance of a transfer learning-based model for brain tumor detection from 253 brain magnetic resonance imaging (MRI) sample images. The concept of transfer learning has been applied using a pretrained Inception V3combined with data augmentation. Modified layers using dropout and regularization have been additionally utilized to deal with overfitting. The proposed method shows an increase in accuracy of 6.430%, a precision of 5.531 %, a recall of 10.545%, and an F1-score of 8.040% compared to the baseline method. We show that our proposed method has been able to effectively enhance performance and reduce overfitting, even with a small number of datasets. Moreover, our proposed method outperforms state-of-The-Art brain tumor detection. © 2024 IEEE.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

MIN-DAS: The Digital Asset System Based on MIN Architecture 4

MIN-DAS: The Digital Asset System Based on MIN Architecture

引用

4th International Conference on Communication technology and information technology, ICCTIT 2024

作者： Chen, Rui Li, Hui Li, Wuyang Yu, Yimin Kaili, Shao Li, Kedan Peking University Shenzhen Graduate School Shenzhen China University of Yunnan Finance and Economics Information School Kunming China Huanghe Science and Technology University Faculty of Engineering Zhengzhou China University of Illinois Urbana-Champaign Department of Computer Science United States

ISBN: (纸本)9798331528973

In this paper, we propose an innovative digital asset system based on the MIN (MIN-DAS), which integrates PPoV consortium blockchain technology along with NFT identifiers, achieving the rights confirmation, decentralization, and high security for digital asset trading. We first deployed the MIN-DAS within the high-security MIN network and designed a built-in MIN-VPN security tunnel, enabling end-to-end communication encryption, thereby significantly bolstering transaction security. Subsequently, we adopted the PPoV consortium blockchain as the core processing engine, greatly increasing the speed of transaction processing to accommodate large-scale transaction demands. Moreover, MIN-DAS supports stablecoin transactions, effectively mitigating risks stemming from cryptocurrency market fluctuations. MIN-DAS provides a secure and efficient solution for digital asset trading. Our experimental findings reveal that, compared to existing traditional digital asset platforms, MIN-DAS demonstrates significant superiority in both performance and security. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Remote Inter-beat Interval Estimation: A Signal Reconstruction Approach with Multi-channel Input and Channel-Wise Attention Mechanism

Remote Inter-beat Interval Estimation: A Signal Reconstructi...

引用

International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Shengze Wang Mondher Bouaziz Tomoaki Ohtsuki Graduate School of Science and Technology Keio University Department of Information and Computer Science Faculty of Science and Technology Keio University Yokohama Japan

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

This paper presents a radar-based heart rate monitoring and Inter-beat interval (IBI) estimation. Traditional IBI estimation relies on peak detection combined with signal processing techniques. Our approach uses signal reconstruction with a neural network, which significantly improves the accuracy and robustness of IBI estimation compared to conventional methods. By utilizing multi-channel input data, our approach effectively mitigates various sources of noise and interference, resulting in highly accurate and reliable IBI predictions. Instead of relying solely on peak detection, we use the U-net architecture and cross-channel attention mechanism to reconstruct the triangular waveform generated from the ground truth ECG signals. The incorporation of multi-channel input and channel-wise attention mechanism improves our model's ability to discriminate and em-phasize critical features from different input channels, further improving IBI estimation accuracy. To evaluate the accuracy and robustness of our proposed method, we trained our model on the open-source dataset [1], and then performed leave-one-subject-out validation, which ensures our approach to be evaluated only on unseen independent subjects. We have achieved a Root Mean Square Error (RMSE) of 26.7 ms for the IBI of each heart-beat. Our results demonstrate the transformative potential of adopting signal reconstruction methods supported by state-of-the-art deep learning techniques. This shift in perspective promises more accurate and robust heart rate and IBI estimation, opening new avenues for improving the accuracy and reliability of cardiac monitoring systems. 1 1 This work was supported by JST ASPIRE Grant Number JPMJAP2326, Japan.

关键词： Heart rate Accuracy Attention mechanisms Noise Neural networks Estimation Signal reconstruction Robustness Root mean square Monitoring

来源：评论

学校读者我要写书评

暂无评论

Toward Digital Transformation Adoption: A Conceptual Framework from Transformational Leadership Perspective

引用

Procedia computer science 2024年 234卷 1175-1182页

作者： Marcel Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Department Universitas Kristen Krida Wacana Jakarta Indonesia Smart Cities and Communities Innovation Center Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey.

关键词： transformational leadership digital transformation digital adoption conceptual framework

来源：评论

学校读者我要写书评

暂无评论

Uncovering the Hidden Insights of the Government AI Readiness Index: Application of Fuzzy LMAW and Schweizer-Sklar Weighted Framework

Decision Making: Applications in Management and Engineering

引用

Decision Making: Applications in Management and Engineering 2024年第2期7卷 443-468页

作者： Nasution, Mahyuddin K. M. Elveny, Marischa Pamucar, Dragan Popovic, Milena Gušavac, Bisera Andric Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia Data Science & Artificial Intelligence Fasilkom-TI Universitas Sumatera Utara Medan20155 Indonesia University of Belgrade Faculty of Organizational Science Department of Operations Research and Statistics Jove Ilica 154 Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan School of Engineering and Technology Sunway University Selangor Malaysia

There is considerable promising in artificial intelligence (AI) and algorithms, with governments worldwide increasingly investing in this transformative technology. The potential benefits include improved performance, cost reduction, efficient management, and crime prediction and prevention, among others. The AI era holds the promise of revolutionizing various aspects of society. However, as countries prepare to leverage the power of artificial intelligence, questions arise about the validity of rankings published on the readiness of the governments for the application of AI. In this article, the weighting criteria that are analysed in the Oxford Insights AI Readiness Index are scrutinized, aiming to provide a more accurate assessment. Instead of conventional averaging, arithmetic and geometric non-linear functions are employed to analyse and assess the rank of the countries. Through clustering analysis, countries are categorized into three distinct groups based on observed criteria, offering a nuanced perspective on government AI readiness. This clustering approach not only facilitates a more effective categorization of countries based on their AI preparedness, but also accentuates the variations and similarities within each cluster, which enables deeper insights into regional trends and pinpoint targeted strategies for enhancement within each cluster. © 2024 Regional Association for Security and crisis management. All rights reserved.

关键词： Cost reduction

来源：评论

学校读者我要写书评

暂无评论

Smart Data Augmentation for Multiclass Brain Tumor Detection with Imbalanced Data 8

Smart Data Augmentation for Multiclass Brain Tumor Detection...

引用

8th International Conference on information technology, information Systems and Electrical Engineering, ICITISEE 2024

作者： Prayogo, Rizal Dwi Nambo, Hidetaka Graduate School of Natural Science and Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Faculty of Transdisciplinary Sciences Kanazawa Japan

ISBN: (纸本)9798350368970

Dealing with class imbalance is a significant issue in classification tasks that often leads to lower prediction performance. Many data augmentation methods have been suggested to tackle this problem, but their effectiveness can vary depending on the dataset. Utilizing augmentation methods can improve the learning and generalization abilities of the proposed convolutional neural network (CNN) model. However, it is challenging to find a single data augmentation method that can effectively address class imbalance problems across all scenarios. In this paper, we present a technique known as smart data augmentation (SDA) that is aimed at effectively improving classification performance by enhancing imbalanced data. SDA involves an equation that can be used to create an augmentation technique, combining different sampling methods for addressing multiclass imbalance and offering easy customization options. We perform the data balancing strategy including undersampling, oversampling, and SDA-based sampling. We evaluate our proposed framework using a CNN model to improve the detection of brain tumors in imbalanced data. Our experiments demonstrate that SDA-based sampling improves brain tumor detection performance by 3.546% in accuracy, 3.310% in precision, 3.432% in recall, and 3.371 % in Fl-score compared to the imbalanced data scenario. The findings suggest that utilizing SDA together with CNN can improve the classification performance of brain tumor detection with imbalanced data. © 2024 IEEE.

关键词： Brain tumor detection class imbalance problem convolutional neural network smart data augmentation

来源：评论

学校读者我要写书评

暂无评论

Massive MIMO Belief Propagation Detection Using DIP with DNN-Trained Scaling Factor

Massive MIMO Belief Propagation Detection Using DIP with DNN...

引用

IEEE Conference on Wireless Communications and Networking

作者： Junta Tachibana Mondher Bouazizi Tomoaki Ohtsuki Graduate School of Science and Technology Keio University Department of Information and Computer Science Faculty of Science and Technology Keio University Yokohama Japan

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Belief propagation (BP) detection is a technique for separating and detecting incoming signals with minimal complexity in massive multiple-input multiple-output (MIMO) systems. However, because of the interference and noise that remain in the received signals even after attempting to remove them through conventional techniques, errors manifest in the transmitted messages. Due to the MIMO channel's numerous loops, a message containing mistakes spreads across the factor graph leading to a degradation in the BP's convergence properties and detection performance. In this paper, we propose a BP detection using deep image prior (DIP) with deep neural network (DNN)-trained scaling factor. By applying DIP to the BP detection algorithm, we achieve a reduction in residual interference and noise. Post DIP application, there is a modification in the variance of both interference and noise components. To align it more accurately with its true value and enhance message reliability, we adjust the variance using scaling factors trained through DNN-based damped BP (DNN-dBP). Using computer simulations, we demonstrate that applying DIP helps decrease the power of the residual interference and noise after removing the interference at each iteration in the BP detection. It is also shown that the proposed method improves the detection performance compared to the normal BP detection, BP detection without DIP when training the scaling factors of the variance, and BP detection to which DIP is applied without training the scaling factors of the variance.

关键词： Training Degradation Noise Interference Massive MIMO Reliability Detection algorithms

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：