检索结果-内蒙古大学图书馆

Leveraging AI-Generated Content for Synthetic Electronic Health Record Generation With Deep Learning-Based Diagnosis Model

引用

IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Abdel-Khalek, Sayed Algarni, Abeer D. Amoudi, Ghada Alkhalaf, Salem Alhomayani, Fahad Mohammed Kathiresan, Shankar Department of Mathematics and Statistics College of Science Taif University Taif Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer College of Science and Arts in Ar Rass Qassim University Ar Rass Saudi Arabia College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Consumer electronics have transformed the way we interact with technology, improving convenience and connectivity in day-to-day lives. In the healthcare sector, recent technologies have resulted in enhanced diagnosis, treatment, and patient care. Wearables, artificial intelligence-based data analytics, and telemedicine transform the way of monitoring and managing health, fostering a proactive approach to well-being. The popularity of ChatGPT is proven great potential for AI-generated content (AIGC) that has formed a major impact on the artificial intelligence (AI) community and accelerates the reconsidering of the prospects of general AI. The AIGC is also exposed as a considerable scope to impulse healthcare electronics (HE). Although generative AI has achieved popularity like the formation of images, it could be employed for producing synthetic tabular information. The production of synthetic electronic health records (EHR) undertakes to increase the utilization of machine learning (ML) methods that commonly function with massive quantities of data. ML will identify non-intuitive classifier patterns that permit a new integration of patient feature predictive ability. Currently, deep learning (DL) techniques are effectively utilized in EHR data from medical domains. DL methods excellently captured the significant and beneficial features and patterns from the comprehensive medical information in EHR data. This study presents AI-generated content for Synthetic Electronic Health Record Generation with a Deep Learning-based Diagnosis (SEHRG-DLD) Model. The focus of the SEHRG-DLD technique is to initially generate the synthetic EHR data and then analyze the medical data for disease diagnosis using the DL model. The SEHRG-DLD technique comprises a two-stage process: synthetic data generation and disease diagnosis. At the initial stage, the SEHRG-DLD technique uses the ChatGPT tool to generate synthetic EHR data. Then, the SEHRG-DLD technique undergoes the disease diagnosi

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

FolkRank++: An optimization of folkrank tag recommendation algorithm integrating user and item information

引用

KSII Transactions on Internet and information Systems 2021年第1期15卷 1-19页

作者： Zhao, Jianli Zhang, Qinzhi Sun, Qiuxia Huo, Huan Xiao, Yu Gong, Maoguo College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao China Faculty of Engineering and Information Technology University of Technology SydneyAustralian Australia Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'an China

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance. © 2021 Korean Society for Internet information. All rights reserved.

关键词： Behavioral research

来源：评论

学校读者我要写书评

暂无评论

Automated Detection and Quantitative Assessment of Extranodal Extension in Lymph Node Breast Tumors Using Dual-Tier Deep Learning and Density-Based Contour Algorithms

Automated Detection and Quantitative Assessment of Extranoda...

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Zhichao Yu Simon James Fong Liming Zhang Tengyue Li Xin Zhang Department of Computer and Information Science University of Macau Faculty of Information North China University of Technology Beijing China Department of Pathology First People’s Hospital of Foshan Guangdong China

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Accurate detection and quantification of extranodal extension (ENE) in lymph node metastases is crucial for effective tumor staging and prognosis. In this paper, we propose a novel dual-tier deep learning-based computational method to automate the assessment of ENE in breast cancer lymph node sections. The method integrates an improved deep learning namely DenseNet-DMSCAMP that was designed for histological cell recognition, to classify cellular regions at the histological level into multiple classes—fat-only (adipose tissue), fat-and-skin (adipose and epithelial cells), skin-only (epithelial cells), and skin-and-immune (immune cells)—and applies a density-based active contour tracing (DB-ACT) algorithm, akin to DBScan, for lymph node boundary detection. Additionally, protrusion measurement algorithms are used to quantify the extent of tumor cell invasion beyond the lymph node capsule. The approach addresses the limitations of manual inspection, which can lead to misdiagnosis due to the visual complexity of whole-slide images (WSIs). Our method was validated on the Camelyon17 Grand Challenge dataset, which comprises H&E stained whole-slide images of lymph node sections. Experimental results showed a discrepancy of 3.9% to 11.7% in estimating ENE length and a position accuracy for breached capsule gaps ranging from 0.27% to 8.6%, demonstrating performance comparable to experienced histologists. These findings highlight the potential of our automated ENE evaluation system to improve diagnostic precision and alleviate the labor-intensive task of manual inspection in clinical settings.

关键词： Deep learning Adaptation models Accuracy Breast tumors Manuals Inspection Distance measurement Breast cancer Classification algorithms Lymph nodes

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Indoor Localization using CSI Technique

Machine Learning for Indoor Localization using CSI Technique

引用

International SoC Design Conference, ISOCC

作者： Wilaiporn Lee Cattareeya Suwanasri Luepol Pipanmekaporn Kanabadee Srisomboon Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Computer and Information Sciences Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

In this research, we propose a low-cost indoor localization technique using the CSI. By using CSI signal as input data, different locations and human activities are classified effectively using machine learning models. According to a low-cost constraint where a single transmitter (Tx) and receiver (Rx) are utilized, we investigate the appropriate transmitter location for achieving accurate position estimation in an indoor environment under two patterns of room environment. Moreover, an appropriate machine learning model for position estimation is examined where decision tree, KNN, Naïve bayes, and ANN are considered. The experimental results demonstrate that the highest accuracy can be achieved using the ANN technique. The shortest distance between a Tx and Rx provides the highest precision for determining the human location in an empty room, whereas placing the Tx and Rx in opposing positions is advantageous for a section with items positioned against the wall. When determining an average accuracy, placing the Tx and Rx in opposing positions shows the highest accuracy among others.

关键词： Location awareness Accuracy Transmitters Estimation Machine learning Receivers Predictive models Nearest neighbor methods Reflection Indoor environment

来源：评论

学校读者我要写书评

暂无评论

Straight-line: A new global path planning algorithm for Mobile Robot 2

Straight-line: A new global path planning algorithm for Mobi...

引用

2nd IEEE International Conference on Electronic Engineering, ICEEM 2021

作者： Nasr, Aida A. El-Bahnasawy, Nirmeen A. El-Sayed, Ayman Tanta University Faculty of Computers and Information Information Technology Dept. Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Dept Egypt

ISBN: (纸本)9781665418423

Today most organizations use mobile robots for many purposes and to perform difficult and repetitive tasks. Robots are used to transport goods from one place to another within factories and warehouses. It is also used to provide different services in government and private institutions, as it can interact with people and help them to obtain different services with high quality and very quickly without any trouble. For the robot performs its functions better, it needs a path planning algorithm to avoid obstacles and works faster. Therefore, we develop a new algorithm called straight line algorithm. The new algorithm can get the optimal path with shortest length in low running time. The new algorithm searches for the optimal solution in the critical region firstly. This way capable of finding the shortest path faster. We evaluate straight-line algorithm with Genetic algorithm GA. We find that the new algorithm outperforms better than the GA in terms, distance, speed, and running time. © 2021 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Next Generation Image Watermarking via Combined DWT-SVD Technique 2

Next Generation Image Watermarking via Combined DWT-SVD Tech...

引用

2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Na'amneh, Qais Almomani, Ammar Nasayreh, Ahmad Nahar, Khalid M.O. Gharaibeh, Hasan Al Mamlook, Rabia Emhamed Alauthman, Mohammad Applied Science Private University Department Of Cyber Security Amman Jordan School Of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I. T. Department Irbid Jordan Yarmouk University Department Of Computer Sciences Irbid211633 Jordan Yarmouk University Faculty Of Information Technology Computer Sciences Irbid211633 Jordan Trine University Dep. Of Business Administration IN United States University Of Zawia Dep.of Mechanical And Industrial Engineering Tripoli Libya University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan

ISBN: (纸本)9798350394962

Personal computers and the Internet are used in different areas and are easier to use. Most data is easy to transmit and duplicate in digital format, and being tampered with and stolen easily leads to issues for content owners and service providers. The Paper describes a secure (tampering-resistant) method for watermarking images for protection and authentication and digital media like audio, video, and multimedia data, and a combination of singular value decomposition (SVD) and Discrete wavelet transform (DWT) for insertion watermark by MATLAB to increase accuracy, simplicity, and flexibility Readability, secure robustness, and imperceptibility the visibility of the image. The proposed algorithm consists of three main steps: Firstly, we decomposed the original image into four sub-bands and the original watermark. Secondly, embed the watermark in the LL sub-band by replacing the singular value for the watermark. Finally, embed a watermark in the other three sub-bands by adding LL values for a watermark in each sub-band in the original image. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Conservative Q-Learning Based Furnace Temperature Control in Municipal Solid Waste Incineration Process

Conservative Q-Learning Based Furnace Temperature Control in...

引用

International Conference on Industrial Artificial Intelligence (IAI)

作者： Hao Tian Jian Tang Heng Xia Jian Zhang Tianzheng Wang Wen Yu Faculty of Information Technology Beijing University of Technology Beijing China School of Computer Science Nanjing University of Information Technology Nanjing China Departamento de Control Automatico CINVESTAV-IPN (National Polytechnic Institute) Mexico City Mexico

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

The nonlinear, time-varying, and lagging nature of the municipal solid waste incineration (MSWI) process presents challenges for ensuring controller safety. While offline reinforcement learning (RL) can ensure safety in furnace temperature (FT) control, its performance is hindered by extrapolation errors, making it unsuitable for direct application in the incineration environment. To address this, we propose a conservative Q-learning-based furnace temperature control strategy (CQL-FTC). This strategy involves two stages: online sampling and offline training. During the online sampling stage, the agent interacts with the environment to collect samples, building an experience replay buffer (ERB) and performing pretraining. In the offline training stage, we introduce the CQL method, adding constraint terms to the traditional Bellman equation to minimize extrapolation errors. After offline training, the agent is directly applied to the FT setpoint control in the incineration process. Simulation results using the actual MSWI process dataset demonstrate the effectiveness of the proposed method in complex industrial environments.

关键词： Training Waste management Waste materials Extrapolation Temperature distribution Incineration Furnaces Process control Temperature control Safety

来源：评论

学校读者我要写书评

暂无评论

Enhancing Sequential Recommendation System For MOOCs Based On Heterogeneous information Networks

Enhancing Sequential Recommendation System For MOOCs Based O...

引用

International Conference on Multimedia Analysis and Pattern Recognition (MAPR)

作者： Long Nguyen Thu Nguyen Khoa Tan-Vo Tu-Anh Nguyen Hoang Tri Nguyen Ngoc-Thanh Dinh Faculty of Computer Science University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam Faculty of Information Science and Engineering University of Information Technology Ho Chi Minh City Vietnam Aalto University Finland The Industrial University Ho Chi Minh City Vietnam

ISBN: (数字)9798350368437

ISBN: (纸本)9798350368444

This study introduces a novel recommendation framework for MOOCs (Massive Open Online Courses)that leverages HINs (Heterogeneous information Networks) to enhance sequential recommendation models. This is the first attempt to utilize HINs in this context, offering a more comprehensive approach than traditional methods that rely solely on homogeneous data sources. The framework employs HERec, an attentive meta-path based approach to capture contextual information and learn nuanced representations of learners and items. By combining HERec with sequential recommendation models like BERT4Rec and SASRec, the system provides personalized course recommendations based on user preferences, implicit relationships, and contextual factors, addressing the challenges of data diversity and sparsity. Experiments demonstrate a significant increase in accuracy, with the highest improvement reaching 12.69%, providing learners with more relevant and personalized course suggestions.

关键词： computer aided instruction Electronic learning Accuracy Soft sensors Multimedia systems Diversity reception Data models

来源：评论

学校读者我要写书评

暂无评论

Follicle Detection Model on Ovarian Ultrasound Image 7

Follicle Detection Model on Ovarian Ultrasound Image

引用

7th International Conference on Informatics and Computing, ICIC 2022

作者： Hartati, Sri Musdholifah, Aina Wulaning Ayu, Putu Desiana Dasuki, Jaswadi Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia Institut Teknologi Dan Bisnis Stikom Bali Faculty Computer and Informatics Department of Information Technology Bali Indonesia Universitas Gadjah Mada Faculty of Medicine Public Health and Nursing Yogyakarta Indonesia

ISBN: (纸本)9798350345711

every woman has two ovaries. Ovaries have several follicles, which consist of oocytes or eggs which are filled with granulosa cells. Some women can have a difference in the number of follicles in each ovary. There are cases of several follicles that are coincided, making it difficult to calculate the number of follicles. In this study, the separation of adjoining follicles and automatic follicle counting was carried out from the results of ovarian ultrasound image segmentation. The segmentation results obtained feature information in the form of follicular feature extraction as many as eight features. The techniques used in this work for feature selection was carried out using Principal Components Analysis (PCA) to reduce the feature. In this study, the PCA and Support Vector Machine (SVM) classifier produced higher accuracy than the classification without PCA. The experimental results also show that the proposed method produced higher classification accuracy than previous work, which yielded 90.39% accuracy, 90.27 % sensitivity, and 90.43 % specificity. © 2022 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Ramifications of the Greenhouse Effect: A Closed Vehicle Example

Ramifications of the Greenhouse Effect: A Closed Vehicle Exa...

引用

International Conference on Smart Systems and Technologies (SST)

作者： Dina Jukić Hrvoje Glavaš Jana Dukić Lukrecia Vulić Department of Core Courses Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Power Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482165

In this contribution, ramifications of the greenhouse effect on a closed vehicle exposed to solar radiation are discussed. The Earth's atmosphere-the vehicle is considered a system which physical background of energy transfer and transformations are analyzed. Based on the physics presented, a product is proposed: barium sulphate window covers that would minimize the effect. Such covers are easy to assemble, can be DIYed, preserve vehicle interior, and lover fuel consumption due to air-conditioning temperatures, which have the second most negative effect on the fuel economy.

关键词： cover energy transfer energy transformation greenhouse effect vehicle

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：