检索结果-内蒙古大学图书馆

8th International Conference on Informatics and Computing, ICIC 2023

作者： Kamardi, Calvin Laksana, I Kadek Perry Bagus Anggreainy, Maria Susan Fitriawati, Nora Yonatan, Michiko Amaya Mangkang, Tesalonika Abigail Eikwine Pengesa, Vianny Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350342604

Alzheimer's Disease (AD) is a brain disorder that causes dementia and affects the memory, cognitive, and behavioral function. Early detection for AD can help to reduce the symptoms and slow down AD progression. Deep learning, particularly Convolutional Neural Network (CNN), a component of artificial intelligence, has demonstrated efficacy in resolving image-related issues and has gained widespread use in the analysis of medical images. CNN works well on large datasets, however, in imbalanced datasets, it can perform poorly by misclassifying minority classes. In this study, the random over-sampling technique is used to address the imbalance in the dataset, while Albumentations is used for image augmentation to prevent overfitting. The results demonstrate that the combination of these techniques leads to improved classification performance compared to traditional approaches. The proposed method has the potential to improve the accuracy of AD diagnosis and contribute to the development of effective treatments for this debilitating condition. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Hi-Le and HiTCLe: Ensemble Learning Approaches for Early Diabetes Detection Using Deep Learning and Explainable Artificial Intelligence

引用

IEEE Access 2024年 12卷 66516-66538页

作者： Shaheen, Ifra Javaid, Nadeem Alrajeh, Nabil Asim, Yousra Aslam, Sheraz Comsats University Islamabad Department of Computer Science Islamabad44000 Pakistan National Yunlin University of Science and Technology International Graduate School of Artificial Intelligence Yunlin Douliu64002 Taiwan King Saud University College of Applied Medical Sciences Department of Biomedical Technology Riyadh11633 Saudi Arabia Limassol3036 Cyprus

Diabetes is a metabolic disease caused by the body's failure to use insulin or break down meals correctly. Every year, an alarming number of new cases of diabetes are recorded. A poor lifestyle and an unfavorable environment are the two main causes of diabetes. If it is not treated at early stages, it becomes a lifelong disease and further leads to failure of important organs such as the kidneys, heart, eyes, and so on. This danger can be decreased with timely and precise identification. Deep Learning (DL) is the best method for illness prediction, as demonstrated by recent developments in DL for clinical use. We have proposed two ensemble learning approaches: blending and hybrid by using the Diabetes Prediction Dataset (DPD), which is a highly imbalanced dataset. The number of diabetic patients in it are 8500 whereas, the number of non-diabetic individuals are 91500. To overcome the class imbalance problem, a Proximity-Weighted Synthetic Oversampling (ProWSyn) technique is implemented. We have proposed a hybrid of highway and LeNet model, named Hi-Le, for early and accurate diabetes detection. Hi-Le model achieves an accuracy of 94%, a F1-Score of 96%, precision score of 94% and recall of 95% and beats its individual models in terms of accuracy, F1-Score, precision and recall. We have also proposed a blending model named HiTCLe using Highway, LeNet, and a Temporal Convolutional Network (TCN) to detect and predict diabetes at an early stage. HiTCLe performs best, beats its individual models, highway, TCN and LeNet, and achieves an accuracy score of 94% and a F1-Score of 94%, whereas individual models achieve an accuracy score between 89% and 91% on 10 epochs. To validate models' results, we have implemented K-Fold Cross Validation (K-FCV). Also, to know the features contributions, we have implemented Shapley Additive eXplanations (SHAP) post processing technique. Both ensemble learning models outperform their individual models in term of accurate diabetes detection

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Efficient k-Center Algorithms for Planar Points in Convex Position 1

引用

18th International Symposium on Algorithms and Data Structures, WADS 2023

作者： Choi, Jongmin Lee, Jaegun Ahn, Hee-Kap CryptoLab Seoul Korea Republic of Department of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of Graduate School of Artificial Intelligence Department of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of

ISBN: (数字)9783031389061

ISBN: (纸本)9783031389054

We present an efficient algorithm for the planar k-center problem for points in convex position under the Euclidean distance. Given n points in convex position in the plane, our algorithm computes k congruent disks of minimum radius such that each input point is contained in one of the disks. Our algorithm runs in O(n2min { k, log n} log n+ k2nlog n) time. This is the first polynomial-time algorithm for the k-center problem for points in convex position. For any fixed integer k, the running time is O(n2log n). Our algorithm works with little modification for the k-center problem of points in convex position under the Minkowski distance of order p for any fixed positive integer p. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Polynomial approximation

来源：评论

学校读者我要写书评

暂无评论

Innovative Outlier Detection with SEBOOST: A Novel Hybrid Ensemble Approach 4

Innovative Outlier Detection with SEBOOST: A Novel Hybrid En...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Son, Nguyen Khanh Sangaiah, Arun Kumar Medhane, Darshan Vishwasrao International Graduate School Of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350352931

Outlier detection is critical in numerous domains such as financial fraud detection, cybersecurity, and system surveillance. This study introduces SEBOOST, a novel hybrid ensemble algorithm that combines supervised and unsupervised machine learning techniques for improved outlier detection. The method leverages sequential ensembles to generate enriched feature spaces, applies feature selection using information gain to remove irrelevant features, and employs a supervised CATBoost classifier for final predictions. SEBOOST was rigorously tested on 21 real-world datasets, demonstrating its superior performance over state-of-the-art methods with higher precision, true positive rate, and AUC-ROC scores. These results indicate that SEBOOST as a robust and scalable framework for addressing outlier detection challenges across diverse applications. © 2024 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Layer-Wise External Attention by Well-Localized Attention Map for Efficient Deep Anomaly Detection

引用

SN computer science 2024年第5期5卷 1-13页

作者： Nakanishi, Keiichi Shiroma, Ryo Hayakawa, Tokihisa Katafuchi, Ryoya Tokunaga, Terumasa Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka-ken Iizuka-shi 820-8502 Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka-ken Iizuka-shi 820-8502 Japan

The external attention mechanism offers a promising approach to enhance image anomaly detection (Hayakawa et al., in: IMPROVE, pp. 100-–110, 2023). Nevertheless, the effectiveness of this method is contingent upon the judicious selection of an intermediate layer with external attention. In this study, we performed a comprehensive series of experiments to clarify the mechanisms through which external attention improves detection performance. We assessed the performance of the LEA-Net (Hayakawa et al., in: IMPROVE, pp. 100–110, 2023), which implements layer-wise external attention, using MVTec AD and Plant Village datasets. The detection performances of the LEA-Net were compared with that of the baseline model under different anomaly maps generated by three unsupervised approaches. In addition, we investigated the relationship between the detection performance of LEA-Net and the selection of an attention point, which means an intermediate layer where external attention is applied. The findings reveal that the synergy between the dataset and the generated anomaly map influenced the effectiveness of the LEA-Net. For poorly localized anomaly maps, the selection of the attention point becomes a pivotal factor in determining detection efficiency. At shallow attention points, a well-localized attention map successfully notably improves the detection performance. For deeper attention points, the overall intensity of the attention map is essential;this intensity can be substantially amplified by layer-wise external attention, even for a low-intensity anomaly map. Overall, the results suggest that for layer-wise external attention, the positional attributes of anomalies hold greater significance than the overall intensity or visual appearance of the anomaly map. © The Author(s) 2024.

关键词： Anomaly detection Deep learning MVTec AD Plant science Self-attention Visual attention mechanism Visual inspection AI

来源：评论

学校读者我要写书评

暂无评论

The Effectiveness of CNN-Based Models in Macroscopic Motion Direction Detection 6

The Effectiveness of CNN-Based Models in Macroscopic Motion ...

引用

6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Qiu, Zhiyu Chen, Tianqi Hua, Yuxiao Todo, Yuki Graduate School of Natural Science & Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Kanazawa Japan

ISBN: (纸本)9798331527426

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, offering significant advancements in image and video recognition, classification, and segmentation tasks. Leveraging hierarchical pattern recognition capabilities, CNNs have demonstrated superior performance over traditional machine learning methods. One particularly critical application of CNNs is in motion direction detection, which is essential for enhancing security in surveillance systems, ensuring safe navigation in autonomous vehicles, and providing valuable insights in sports analytics. In this study, our primary goal was to investigate how different types of input data impact the accuracy of motion direction detection. We applied five distinct preprocessing methods to test the effect of input data on accuracy. Furthermore, we conducted experiments in various object and background scenarios to evaluate adaptability of simple 4-layer CNN, EfficientNetBO and ResNet50 in detecting motion direction. Finally, we tested the noise resistance of these three models. The results indicate that CNNs are capable of extracting the macroscopic motion direction of objects in images. Our study highlights the potential of CNN-based models for efficient and effective motion direction detection. © 2024 IEEE.

关键词： Convolutional neural networks

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) 9

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

引用

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

作者： Wijayanti, Diana Urbaya, Satriawansyah Sitompul, Taufiq Adrian, Verry BINUS Graduate Program Bina Nusantara University Department of Information Systems Jakarta11480 Indonesia Program USAID Indonesia Jakarta12930 Indonesia University of Oslo Department of Informatics Oslo0316 Norway Binus Graduate Program - Doctor Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta Provincial Health Office Center of Data and Information Technology Jakarta10160 Indonesia

ISBN: (纸本)9798350390025

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. © 2024 IEEE.

关键词： e-government

来源：评论

学校读者我要写书评

暂无评论

Smart Tourism Destinations Research Trend: A Systematic Literature Review 9

Smart Tourism Destinations Research Trend: A Systematic Lite...

引用

9th International Conference on ICT for Smart Society, ICISS 2022

作者： Elda Hiererra, Siti Meyliana Ramadhan, Arief Purnomo, Fredy Graduate Program Computer Science School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

Purpose - This study aims to identify and analyze the main themes, the countries, the destination category, and the current trend regarding Smart Tourism research through a systematic literature review (SLR). Methodology- The research is based on a systematic review of the literature using the kitchenham method combined with the PRISMA method and focuses on smart tourism destination articles published between 2018 and 2021. Findings- The authors summarize the result, which is the main themes came out with seven clusters;the countries, there are eighteen countries spread over five regions worldwide;the destination category found three categories which are cultural heritage attractions, special events, and museums;the current trends there are top five keywords that become trends based on previous study literature review namely: Smart Tourism Destinations, Tourist Experiences, Tourism Industry, ICT, and DMOs. The originality of the research- The study provides the SLR method to investigate the main themes, the countries, the destination category, and the current trend regarding Smart Tourism research. This finding helps build a complete understanding of smart tourism destination implementation comprehensively. © 2022 IEEE.

关键词： Tourism

来源：评论

学校读者我要写书评

暂无评论

The Impact of Information System and technology of Courier Service During Pandemic 7th

The Impact of Information System and Technology of Courier S...

引用

7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Siahaan, Andre Timiko Pinajung, Bagas Rizkyka Kennidy Gaol, Ford Lumban Matsuo, Tokuro School of Information System Bina Nusantara University Alam Sutra Tangerang13960 Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819951659

Courier services are a means of transport that can be used to deliver orders to customers or claim orders from them, and customers can use technology from the courier service like track order to track their order. The use of courier service nowadays is common that every day, people use courier service as their daily means of transport for delivering goods. However, in situations like pandemic, the courier services need to put more effort into their services, so that customers can use courier services with trust. The courier service must be more careful to deliver customer orders or send orders to customers. For this research methodology, the data collection technique using Google Forms and collecting data directly from people about their opinion toward the research have been employed. The result of the data collecting technique is depicted as the usage of the courier service during COVID-19 pandemic starting from March 2020. The results vary every month because not everyone uses the courier service every day. Moreover, the conclusion on why the months starting from March to June do not show high usage of courier service is that the pandemic has not been that worse than the later months, and so, people were going out to deliver or to do activities like shopping or buying groceries on their own. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information systems

来源：评论

学校读者我要写书评

暂无评论

Cooperative MIMO Applied Null Beamforming to Self in Full-Duplex Wireless Communication System and Its Implementation 24

Cooperative MIMO Applied Null Beamforming to Self in Full-Du...

引用

24th International Conference on Advanced Communication technology, ICACT 2022

作者： Kawano, Masahiro Nagao, Yuhei Lanante, Leonardo Kurosaki, Masayuki Ochi, Hiroshi Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Japan

ISBN: (纸本)9791188428090

This paper proposes a full-duplex wireless communication system applying cooperative multiple-input multiple-output (Co-MIMO). This proposal solves self-interference (SI) in full-duplex wireless communication systems by applying the block diagonalization used in multi-user MIMO (MU-MIMO). Therefore, there is no need for a radio frequency (RF) canceller, which has a large hardware size and is difficult to extend to MIMO. Furthermore, higher throughput can be achieved by applying Co-MIMO, in which multiple access points work together to communicate. We also introduce the implementation of the proposed method using a field-programmable gate array (FPGA) and software-defined radio (SDR) in this paper. It shows that SI cancellation can be realized by using them. The proposed method is implemented based on the IEEE 802.11n frame format as an example. In the experiment, a total of 30 [dB] SI cancellation is confirmed with a transmission power of-12[dBm]. The SI cancellation amount can be further increased by increasing the transmission power. © 2022 Global IT Research Institute-GiRI.

关键词： MIMO systems

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：