检索结果-内蒙古大学图书馆

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Feng, Tiantian Narayanan, Shrikanth University of Southern California Department of Computer Science Los AngelesCA United States University of Southern California Department of Electrical Engineering Los AngelesCA United States

ISBN: (纸本)9798350371499

Medical residency training is often associated with physically intense and emotionally demanding tasks, requiring them to engage in extended working hours providing complex clinical care. Residents are hence susceptible to negative psychological effects, including stress and anxiety, that can lead to decreased well-being, affecting them achieving desired training outcomes. Understanding the daily behavioral patterns of residents can guide the researchers to identify the source of stress in residency training, offering unique opportunities to improve residency programs. In this study, we investigate the workplace behavioral patterns of 43 medical residents across different stages of their training, using longitudinal wearable recordings collected over a 3-week rotation. Specifically, we explore their ambulatory patterns, the computer access, and the interactions with mentors of residents. Our analysis reveals that residents showed distinct working behaviors in walking movement patterns and computer usage compared to different years in the program. Moreover, we identify that interaction patterns with mentoring doctors indicate stress, burnout, and job satisfaction. © 2024 IEEE.

关键词： Personnel training

来源：评论

学校读者我要写书评

暂无评论

Exploring The Efficacy of Convolutional Neural Networks in Predicting Cardiovascular Disease Risk Factors from Biomedical Imaging Data

Exploring The Efficacy of Convolutional Neural Networks in P...

引用

2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Muruguiyan, Akshara Dhasmana, Gulshan Al-Farouni, Mohammed Badhoutiya, Arti Singh, Balpreet Aswini, S. St. Joseph's College Of Engineering & Technology Thanjavur India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Electrical Engineering Mathura India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This exploration investigates the adequacy of convolutional neural networks (CNNs) in foreseeing cardiovascular disease (CVD) risk factors utilizing biomedical imaging information. Utilizing a different dataset including X-ray, CT, and ultrasound pictures close by clinical metadata, four CNN structures (CNN, Reset, Dense Net, and VGGNet) were executed and assessed. DenseNet arose as the top-performing model, accomplishing an exactness of 88.1%, accuracy of 90.1%, review of 86.2%, and F1 score of 88.0%. These outcomes show the prevalent prescient exhibition of Dense Net in contrast with different structures. The review adds to the progression of cardiovascular gamble evaluation techniques, featuring the capability of CNN-based approaches for further developing clinical independent direction and patient results. Pushing ahead, future examination bearings incorporate the investigation of bigger and more different datasets, as well as improving model interpretability and coordinating multimodal imaging information and clinical biomarkers. © 2024 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

AI/ML-Enhanced Security Monitoring for 5G-Enabled Big Data Sensor Networks 32

AI/ML-Enhanced Security Monitoring for 5G-Enabled Big Data S...

引用

32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Da Silva, Stefania E. Iza Rodriguez, Demostenes Z. Rosa, Renata L. Adasme, Pablo Saadi, Muhammad Federal University of Lavras Lavras Department of Computer Science Minas Gerais Brazil Universidad de Santiago de Chile Department of Electrical Engineering Santiago de Chile Chile University of Central Punjab Department of Electrical Engineering Punjab Pakistan

ISBN: (纸本)9789532901382

The integration of 5G technology with sensor network systems has enabled extensive connectivity and data exchange between cyber and physical entities. However, this connectivity brings significant challenges in managing vast amounts of data, including issues related to data duplication, scalability, and security. In this paper, we propose an advanced AI/MLbased algorithm for enhancing the security of 5 G -enabled sensor networks within a big data environment. Our approach leverages both geographical and temporal data to improve the extraction of critical information from multidimensional time-series data, thereby accurately modeling and analyzing attack patterns. The proposed method achieved an overall detection accuracy of 9 3. 3 %, demonstrating a good performance in detecting cyber threats. Additionally, the model exhibited scalability by maintaining high throughput with increasing numbers of sensor nodes. The results ensure the safety, reliability, and scalability of 5G-enabled sensor networks in real-world scenarios. © 2024 University of Split, FESB.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

A Capacitive Backscatter System for Intra-Body Identification

IEEE Journal of Radio Frequency Identification

引用

IEEE Journal of Radio Frequency Identification 2025年 9卷 308-319页

作者： Mohammed, Noor Jackson, Robert W. Lee, Sunghoon Ivan Gummeson, Jeremy University of Massachusetts Amherst Electrical and Computer Engineering department United States University of Massachusetts Amherst Electrical and Computer Engineering United States University of Massachusetts Amherst College of Information and Computer Science United States University of Massachusetts Amherst Electrical and Computer Engineering United States

In this paper, we present a new intra-body communication technology that uses capacitive backscatter. The main goal of this technology is to allow for the transmission of binary IDs between a skin-coupled transceiver and a batteryless tag by utilizing finite conductivity of skin and air coupling capacitance. The intra-body identification (IBID) hardware system consists of two pairs of skin-coupled 50 ohm galvanically isolated electrodes: the interrogator(fixed size: 30 cm × 40 cm) and the tag (arbitrary shape and size). The flexibility of electrode shape enables the IBID tag to be easily deployed on various everyday objects. The interrogator is connected to a battery-powered wearable transceiver. The study investigated the capacitive backscatter phenomenon using two everyday object models: a remote control model and a rectangular single switch panel. The experimental results demonstrate the hardware system’s ability to interrogate binary IDs seamlessly using 40 MHz pulsed radio frequency (RF) carrier with 33% duty cycle. However, the variable dimensions of the tag electrode lead to varying path gain within a short body channel, resulting in low available power for the tag. To address this challenge, we developed an ultra-low powered IBID tag that can sustain its operation at -11dBm peak RF power and transmit multiple bursts of 16-bit binary packets. © 2017 IEEE.

关键词： Backscattering

来源：评论

学校读者我要写书评

暂无评论

Cirrhosis Disease Classification by using Polynomial Feature and XGBoosting 13

Cirrhosis Disease Classification by using Polynomial Feature...

引用

13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Goyal, Agam Zafar, Almaas Kumar, Manav Shashank Bharadwaj, R. Tejas, B.K. Malik, Jahan Gurukul Kangri University Department of Computer Science Engineering Uttarakhand India Maharaja Agrasen Institute of Technology Department of Electrical and Electronics Engineering Delhi India Department of Computer Science and Engineering Karnataka India Department of Information Science and Engineering Karnataka India Vellore Institute of Technology Department of Computer Science Engineering Tamil Nadu India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Every year, over 2.8% of adult fatalities in India are caused by liver diseases. Discovering the liver illness is tough since its early phases, which will exhibit modest symptoms, are difficult to diagnose. Most often, cirrhosis disease doesn't manifest symptoms until an advanced stage, making it challenging to identify and diagnose. This paper shows different approaches to how they analyzed and visualized the performance. Medical issues can be resolved quickly and at a lower cost with the use of machine learning techniques. The major goal of this study is to increase the accuracy of result prediction and lower the price of diagnosis in the medical industry. As a result, we classified individuals with cirrhosis illness using several categorization methods. The biomedical field's use of machine learning for the prognosis and diagnosis of cirrhosis disease has had a massive effect. In the biomedical area, machine learning provides a guarantee for better illness identification and prognosis, as well as for increasing the neutrality of the judgment process. © 2022 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Review Analysis of Distinct Tools Image Plagiarism Detection

A Comprehensive Review Analysis of Distinct Tools Image Plag...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Naudiyal, Anjali Singh, Vineeta Joshi, Kapil Roka, Sanjay Mamoria, Pushpa Shakya, Rajeev Kumar School of Applied and Life Sciences Uttaranchal University Premnagar Dehradun India School of Engineering and Technology Chhatrapati Shahu Ji Maharaj University Department of Computer Science and Engineering Uttar Pradesh Kanpur208024 India Uttaranchal University Department of Computer Science & Engineering Dehradun India Graphic Era deemed to be university dehradun India School of Electrical Engineering and Computing Adama Science and Technology University Department of Electronics and Communication Engineering Adama Ethiopia

ISBN: (纸本)9798350379716

In the era of artificial intelligence, academic activities tend to use Chat GPT more frequently. A Chabot called Chat GPT is used for brainstorming, writing, and learning. Although most academicians use Chat GPT to their advantage when taking notes, it is detrimental to research. A few authors wrote the papers swiftly by using AI techniques. These days, chat GPT is employed in a wide range of tasks. It prepares any type of content for the internet. The likelihood of plagiarism is rising as a result of its use. At present, plagiarism is the biggest challenge in the field of research. Plagiarism occurs when an author uses this technology to modify another person's work and transmit it off as their own without providing a reference or credit. Plagiarism is a term used to describe when someone copies something from another source without providing credit to the original author. Google is working quickly in the image field, but at that point, it will be used to detect plagiarism that is generated with the aid of artificial intelligence. The less papers that are utilized in favor of online resources, the more academics are working to make the globe more environmentally friendly. In the presented paper, a few software applications are discussed and compare a number of different techniques to detect the plagiarism in academics field like scientific equations or cross-lingual or multilingual texts. Various applications are developed in research to identify instances of plagiarism in research papers, assignments, and source code. After reviewing the papers pertaining to various tools for detecting plagiarism, not much more work is done in the area of detecting plagiarism in images or in image text. The article that is being presented comes to the conclusion that no software tool has been developed to identify plagiarism in images or in the text associated with images. © 2024 IEEE.

关键词： Application programs

来源：评论

学校读者我要写书评

暂无评论

Dementia Insights: A Context-Based MultiModal Approach

arXiv

引用

arXiv 2025年

作者： Mehdoui, Sahar Sinene Bouzid, Abdelhamid Sierra-Sosa, Daniel Elmaghraby, Adel Department of Computer Science and Engineering University of Louisville United States Department of Electrical Engineering and Computer Science The Catholic University of America United States

Dementia, a progressive neurodegenerative disorder, affects memory, reasoning, and daily functioning, creating challenges for individuals and healthcare systems. Early detection is crucial for timely interventions that may slow disease progression. Large pre-trained models (LPMs) for text and audio, such as Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), and Contrastive Language-Audio Pretraining (CLAP), have shown promise in identifying cognitive impairments. However, existing studies generally rely heavily on expert-annotated datasets and unimodal approaches, limiting robustness and scalability. This study proposes a context-based multimodal method, integrating both text and audio data using the best-performing LPMs in each modality. By incorporating contextual embeddings, our method improves dementia detection performance. Additionally, motivated by the effectiveness of contextual embeddings, we further experimented with a context-based In-Context Learning (ICL) as a complementary technique. Results show that GPT-based embeddings, particularly when fused with CLAP audio features, achieve an F1-score of 83.33%, surpassing state-of-the-art dementia detection models. Furthermore, raw text data outperforms expert-annotated datasets, demonstrating that LPMs can extract meaningful linguistic and acoustic patterns without extensive manual labeling. These findings highlight the potential for scalable, non-invasive diagnostic tools that reduce reliance on costly annotations while maintaining high accuracy. By integrating multimodal learning with contextual embeddings, this work lays the foundation for future advancements in personalized dementia detection and cognitive health research. © 2025, CC BY.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Machine learning based Parkinson’s disease detection and progression evaluation using gray and white matter segmentation from 3D MRI

引用

Journal of engineering and Applied science 2024年第1期71卷 190页

作者： Islam, Nair Ul Khanam, Ruqaiya Department of Computer Science & amp Engineering Centre of Excellence for Artificial Intelligence in Medicine Imaging & ampForensics Sharda University Greater Noida India Department of Electrical Electronic & ampCommunication Engineering Centre of Excellence for Artificial Intelligence in Medicine Imaging & ampForensics Sharda University Greater Noida India

Parkinson’s disease (PD) is a movement-related neurological condition caused by the death of brain nerve cells that produce dopamine. T1 MR images were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database. Data was collected at baseline, and at 48 months duration. SPM12 software was used to segment gray matter (GM) and white matter (WM) from the MR images. For the classification of PD, CNN and machine learning (ML) classifiers were used to train on the segmented GM and WM. The findings demonstrated that utilizing the segmented GM and WM obtained from MR images at 48 months had a better predictive ability than the data obtainment at the baseline. CNNs did not perform as well as the conventional ML algorithms, especially for the baseline data. This result is most likely due to the smaller dataset available for training the CNNs, as CNNs normally require more data for best performance. Overall, machine learning algorithms were able to distinguish between people with Parkinson’s disease and healthy controls by analyzing GM and WM in brain scans. The classifiers trained at different stages demonstrated varying degrees of accuracy, with the predictive accuracy for the 48-month dataset surpassing that of the baseline data by a significant margin. The accuracy rate for GM was 65.78% at baseline and improved significantly to 92.59% at 48 months. Similarly, the accuracy rate for WM was 60.52% at baseline and improved to 88.89% at 48 months. © The Author(s) 2024.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Multiclass Cricket Shot Classification using CNN 26

Multiclass Cricket Shot Classification using CNN

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ahmed, Mirza Sabbir Hasan, Md Nahid Zinnah Ayon, Walid Ibn North South University Department of Electrical & Computer Engineering Dhaka Bangladesh Deggendorf Institute of Technology Departmentof Building Products and Processes Deggendorf Germany Chittagong University of Engineering & Technology Department of Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798350359015

An acknowledgement of context-based advertising for the audience watching cricket can involve identifying the game of cricket based on the various batting shots, creating sensor-based commentary systems, and creating coaching assistants. This research identifies different cricket shot problems from a vast-scale cricket shot video dataset. The study uses a large dataset of annotated cricket shots that spans a variety of game formats and captures different shot types and player styles. Some cricket shots are the action we find most attractive in cricket videos. Those shots are often captured by cameras installed at the cricket ground's stands at both ends of the cricket pitch. To identify 4 different cricket batting shots from offline footage, this research paper presents a novel approach to classify cricket shots based on deep learning techniques. In this research, we propose a multi-stage classification pipeline for cricket shots, which combines dimensionality reduction, feature extraction, and classification techniques of cricket shots. Before classification, we have preprocessed the dataset that we prepared. The image data was extracted from the video containing around 3000 image frames. The dataset has four classes: i.e. pull shot, square drive, straight drive, and uppercut. The preprocessing steps are normalization, resize, and histogram equalization. After that a scratch model is designed and the model is trained based on the architecture of the scratch model. Finally, we have found the model's accuracy for our prepared dataset is 98.18 percent. © 2023 IEEE.

关键词： batting shots classification convolutional neural network deep learning

来源：评论

学校读者我要写书评

暂无评论

Gap minimization for knowledge sharing and transfer

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2023年第1期24卷 1282-1338页

作者： Boyu Wang Jorge A. Mendez Changjian Shui Fan Zhou Di Wu Gezheng Xu Christian Gagné Eric Eaton Department of Computer Science University of Western Ontario Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology Institute Intelligence and Data Université Laval School of Transportation Science and Engineering Beihang University Department of Electrical and Computer Engineering McGill University Department of Computer and Information Science University of Pennsylvania

Learning from multiple related tasks by knowledge sharing and transfer has become increasingly relevant over the last two decades. In order to successfully transfer information from one task to another, it is critical to understand the similarities and differences between the domains. In this paper, we introduce the notion of performance gap, an intuitive and novel measure of the distance between learning tasks. Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e.g., H-divergence or discrepancy distance), we theoretically show that the performance gap can be viewed as a data- and algorithm-dependent regularizer, which controls the model complexity and leads to finer guarantees. More importantly, it also provides new insights and motivates a novel principle for designing strategies for knowledge sharing and transfer: gap minimization. We instantiate this principle with two algorithms: 1. gapBoost, a novel and principled boosting algorithm that explicitly minimizes the performance gap between source and target domains for transfer learning; and 2. gapMTNN, a representation learning algorithm that reformulates gap minimization as semantic conditional matching for multitask learning. Our extensive evaluation on both transfer learning and multitask learning benchmark data sets shows that our methods outperform existing baselines.

关键词： performance gap transfer learning multitask learning regularization algorithmic stability

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：