检索结果-内蒙古大学图书馆

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Bodaghi, Morteza Hosseini, Majid Gottumukkala, Raju Univ Louisiana Dept MCHE Engn Lafayette LA 70504 USA Univ Louisiana Dept C Sci Lafayette LA USA

ISBN: (纸本)9798350372977;9798350372984

Multimodal deep learning methods capture synergistic features from multiple modalities and have the potential to improve accuracy for stress detection compared to unimodal methods. However, this accuracy gain typically comes from high computational cost due to the high-dimensional feature spaces, especially for intermediate fusion. Dimensionality reduction is one way to optimize multimodal learning by simplifying data and making the features more amenable to processing and analysis, thereby reducing computational complexity. This paper introduces an intermediate multimodal fusion network with manifold learning-based dimensionality reduction. The multimodal network generates independent representations from biometric signals and facial landmarks through 1D-CNN and 2D-CNN. Finally, these features are fused and fed to another 1D-CNN layer, followed by a fully connected dense layer. We compared various dimensionality reduction techniques for different variations of unimodal and multimodal networks. We observe that the intermediate-level fusion with the Multi-Dimensional Scaling (MDS) manifold method showed promising results with an accuracy of 96.00% in a Leave-One-Subject-Out Cross-Validation (LOSO-CV) paradigm over other dimensional reduction methods. MDS had the highest computational cost among manifold learning methods. However, while outperforming other networks, it managed to reduce the computational cost of the proposed networks by 25% when compared to six well-known conventional feature selection methods used in the preprocessing step.

关键词： Psychological Stress Detection Early Fusion Intermediate Level Fusion Manifold learning Dimension Reduction Multimodal learning Biometric Signals Facial Landmarks video

来源：评论

学校读者我要写书评

暂无评论

AFHO-DL: Enhancing Energy Efficiency through Resource Allocation AI-enabled WSNs and IoT Integration 3

AFHO-DL: Enhancing Energy Efficiency through Resource Alloca...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Kalaiselvan, S.A. Manoranjini, J. Hemalatha, S. Kumar, M. Lenin Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Vignan Institute of Technology and Sciences Department of Electronics Communication and Engineering Telangana India

ISBN: (纸本)9798350369083

In the realm of AI-enabled Wireless Sensor Networks (WSNs) and Internet of Things (IoT) integration, efficient resource allocation is paramount for enhancing energy efficiency and optimizing data utilization. The dynamic nature of environments poses challenges to achieving optimal performance measures, necessitating sophisticated solutions. To address this, we propose the AFHO-DL model, which combines the Fire Hawk Optimizer (FHO), UNet architecture, and Multi-objective Jaya algorithm. The FHO algorithm, inspired by the foraging behavior of Fire Hawks, efficiently optimizes resource allocation in WSNs by iteratively updating node positions based on objective functions. Leveraging FHO, we fine-tune the parameters of the UNet architecture, a deep learning model widely used in image processing applications. The UNet architecture, consisting of encoder and decoder paths, extracts features and reconstructs images, enhancing adaptability to various data types. Furthermore, the Multi-objective Jaya algorithm is employed to refine solutions by striking a balance between exploration and exploitation in the solution space, further improving resource allocation strategies. Our experimental results demonstrate the effectiveness of the proposed AFHO-DL model in enhancing energy efficiency and optimizing data utilization in dynamic environments. Through post-processing and visualization, we evaluate the performance of the optimization algorithms, generating Pareto-optimal solutions that represent the best trade-offs between competing goals. The AFHO-DL model facilitates intelligent decision-making and resource allocation strategy optimization, ensuring smooth data transfer and improving network efficiency in AI-enabled WSNs coupled with IoT platforms. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Research priorities for diagnostic instrumentation in urinary incontinence

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF engineering IN MEDICINE 2024年第6期238卷 682-687页

作者： Gammie, Andrew Wachter, Stefan De Bristol Urol Inst Bristol England Antwerp Univ Hosp Antwerp Belgium Bristol Urol Inst Southmead Hosp 3rd Floor L&R Bldg Bristol BS10 5NB England

The international Consultation on Incontinence (ICI) captures current evidence on incontinence. The conference 'Incontinence: The engineering Challenge XIII' in November 2021 heard an update on the most recent ICI summary on urodynamic testing. This paper summarises the ICI recommendations for future research in urodynamics, with a view to informing engineers of issues and challenges that could benefit from engineering solutions. Engineers are encouraged to contribute to the following areas of research, which will have a direct and positive effect on patients' quality of life and overall health: (a) Urine flow measurement: home- and app-based devices, machine learning analysis of flow shape, (b) Pressure measurement: normal values for and validation of new technologies, including air-filled, non-invasive and urethral pressure reflectometry, (c) Ultrasound imaging: bladder wall biomechanics, bladder shape analysis, (d) Assess normal and abnormal value ranges, and diagnostic performance and (e) Specific trials in understudied patient groups including those with symptoms resistant to treatment, children and the frail elderly. Graphical abstract

关键词： Physiological measurement diagnostic monitoring continence technology research diagnostic instrumentation

来源：评论

学校读者我要写书评

暂无评论

Prediction of Thyroid Disease Using machine learning Algorithms 3

Prediction of Thyroid Disease Using Machine Learning Algorit...

引用

3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Vinodhini, V. France, K. Hindustan Institute of Technology and Science Department of Computer Applications India

ISBN: (纸本)9798350399264

The detection and prognosis of diseases is one of the most significant uses of machine learning. Recently, disease classification and identification have benefited greatly and been significantly influenced by machine learning algorithms. One of these problems should cause people to worry about their health because the thyroid gland regulates metabolism, which is vital for maintaining human health. For the purpose of diagnosing thyroid disease, this study employed four machine learning methods: SVM, LR, DT & NB. According to this study, there are three types of thyroid disease: hyper, hypo, and normal. The laboratories of an Iraqi hospital provided the 2690 records that served as the basis for this investigation's data. machine learning algorithms have shown promise in the diagnosis of thyroid disorders, assisting physicians and healthcare professionals in making an early diagnosis and improving the likelihood of therapy. Using all the features, Decision tree algorithm produced the greatest accuracy of 98.93%, however the Logistic regression approach earned the highest accuracy of 95.73% by removing three qualities (query thyroxine, query hypothyroid, and query hyperthyroid). © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

A Survey on Cognitive Radio Network Spectrum Sensing using Deep learning Methods 3

A Survey on Cognitive Radio Network Spectrum Sensing using D...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Nandhini, P. Vimalnath, S. Dept of Electronics and Communication Engineering Tamilnadu Karur India Dept of Electronics and Communication Engineering Tamilnadu Karur India

ISBN: (纸本)9798350375190

A potential novel strategy for addressing the increasing demand for spectrum sensing in cognitive radio networks (CRNs) involves utilizing underutilized frequency bands through spectrum sensing. Spectrum sensing, a fundamental component of CRNs, detects available spectrum bands without disrupting signals from licensed users. Spectral sensing has recently emerged as a domain for deep learning algorithms, which have the capability to autonomously differentiate complex patterns and representations from raw spectrum data. This study conducts a comprehensive review of the state-of-the-art in deep learning for CRN spectrum sensing. It begins with an introductory overview of spectral sensing in CRNs and proceeds to assess several attempts focusing on the integration of deep learning into spectrum sensing. Further, the paper explores into crucial research challenges and potential opportunities associated while employing deep learning for spectrum sensing in CRNs. These challenges encompass issues such as data scarcity, adaptability to diverse environments, energy efficiency, resilience to interference and noise, and real-time processing constraints. The utilization of deep learning for spectrum sensing in CRNs represents an emerging field, and this review includes current methodologies, challenges, and potential remedies in this domain. © 2024 IEEE.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Improving Sleep Health Through data-Driven machine learning Insights 3

Improving Sleep Health Through Data-Driven Machine Learning ...

引用

3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Kapoor, Ananya Suyal, Priyanka Bhardwaj, Rakhi Gola, Kamal Kumar Kumar, Mohit Verma, Aman College of Engineering Roorkee Dept. of CSE Roorkee India COER University Dept. of AI-ML Roorkee India COER University Dept. of CSE Roorkee India College of Engineering Roorkee Dept. of AI-ML Roorkee India

ISBN: (纸本)9798331523893

Sleep is a critical component of overall health and well-being, yet sleep disorders and poor sleep quality affect millions worldwide. Traditional approaches to sleep assessment, often reliant on self-reports and polysomnography, can be costly and impractical for long-term monitoring. Our approach includes machine learning algorithms, including random forests, gradient boosting, and data visualization techniques, are employed to predict sleep stages and identify factors influencing sleep quality. Experimental results demonstrate the efficacy of the proposed models, achieving high accuracy in sleep stage classification and actionable insights for sleep improvement. This work underscores the potential of machine learning to revolutionize sleep health management by providing scalable, data-driven, and personalized solutions. © 2025 IEEE.

关键词： Sleep research

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of Overseas Communication Path for Chinese Corporate Image Based on Multimodal Discourse Analysis with machine learning Algorithm 24

Design and Implementation of Overseas Communication Path for...

引用

3rd international conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Lin, Jiling Lei, Fei Liu, Weiliang School of Foreign languages Guangdong University of Science & Technology Guangdong Dongguan China School of Finance and Economics Guangdong University of Science & Technology Guangdong Dongguan China Huizhou Modern Vocational Technology School Guangdong Huizhou China

ISBN: (纸本)9798400707308

In an increasingly globalized economy, the communication of corporate image plays a pivotal role in shaping perceptions and fostering relationships with international stakeholders. This study explores the design and implementation of effective overseas communication strategies for Chinese corporations, leveraging multimodal discourse analysis and machine learning algorithms. By synthesizing quantitative and qualitative data, we analyze various communication modalities—including visual, textual, and auditory elements—utilized in corporate branding across different cultural contexts. Employing machine learning techniques, we identify patterns and insights from vast datasets, enabling a nuanced understanding of how multimodal elements influence audience reception and engagement. Our findings reveal key factors that contribute to successful corporate image dissemination and highlight the importance of cultural sensitivity in communication strategies. The research provides a framework for Chinese corporations to enhance their global presence by integrating data-driven insights into their communication practices, ultimately fostering a more favorable corporate image and stronger international relationships. This study not only contributes to the field of corporate communication but also offers practical implications for practitioners seeking to navigate the complexities of multicultural environments in an era of rapid technological advancement. © 2024 Copyright held by the owner/author(s).

关键词： machine learning Algorithms

来源：评论

学校读者我要写书评

暂无评论

An on-line detection method of pork quality using hyperspectral imaging combined with machine learning 24

An on-line detection method of pork quality using hyperspect...

引用

3rd international conference on Public Health and data Science, ICPHDS 2024

作者： Tan, Chunping Liang, Guan Yang, Yan Wang, Yafei Wei, Zhongyu Nanning University Guangxi Nanning China Guangxi Yongzhitai Industrial Co. Ltd. Guangxi Nanning China Guangxi State Farms Meat Industrial Services Co. Ltd. Guangxi Nanning China

ISBN: (纸本)9798400711671

In view of the problems such as serious damage, low prediction rate and poor effect of pork quality, the paper integrates the radial basis function neural network and hyperspectral technology, and establishes the prediction model of pork freshness and water content. The outcomes found that the correlation coefficient of pork freshness correction set and prediction set modeling is 0.9983 and 0.9980, respectively, and the prediction effect is better than other comparison models. The correlation coefficients of pork water content correction set and prediction set modeling were 0.9980 and 0.9986, respectively, and the prediction effect was better than the other comparison models. The outcomes denote that RBF is the best prediction effect of pork quality, which lays a foundation for the online rapid nondestructive detection of pork. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Prediction of Parkinson's disease with various ML and DL techniques on speech data 3

Prediction of Parkinson's disease with various ML and DL tec...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Shankar, T. Sreelekha, Gummadapu Tejaswini, Challa Sai Sivasankar, P. Lavanya, N. Murali, J. Vellore Institute of Technology School of Electronics Engineering Vellore India NITTTR Department of ECE Chennai India

ISBN: (纸本)9798350372120

Parkinson's disease(PD) is a degenerative peripheral nervous system syndrome that influences muscle movement. It is incurable. Parkinson's disease impacts more than one million people in India each year. Because of the disease's degenerative nature, symptoms usually appear gradually and are barely perceptible, ranging from ordinary trembling of the hand to apparent speech, and voice, as well as writing alterations to serious symptoms such as loss of automatic movements. This research aims to contribute to the improvement of medical technology by assisting in the advanced and premature identification of PD, permitting experimental therapy by the use of a voice dataset from UCI machine learning(ML) database. We reviewed the current state of PD discernment in this study and discussed the prototype for quick identification of PD using several classifier propositions of ML and Deep learning(DL). The best accuracy of about 94.8% was achieved by the usage of the CNN model with optimizer. We have also further built a web-based GUI for the web-based prediction of PD using model build. © 2024 IEEE.

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

A Regionally Generalized machine learning Framework Towards Census-Enabled Multi-Factor Non-Communicable Disease Analyses 3

A Regionally Generalized Machine Learning Framework Towards ...

引用

3rd international conference on Artificial Intelligence and Software engineering, ICAISE 2024

作者： Geng, Kevin Thota, Sudith Kataria, Anish Independent Researcher Dublin United States Princeton University Princeton United States

ISBN: (纸本)9798331532864

In recent years, non-communicable diseases, diseases intransmittable through human interaction, have cemented themselves as a leading cause of death globally. These diseases are exacerbated by various social, economic and geographical factors within communities. Despite their resounding consequences on community health, the compilation of medical surveys is both costly and time consuming. This study uses a machine learning approach to predict the population proportion with Asthma, High Blood Pressure, Diabetes, and Poor Mental Health within census tract populations in three states. Through an evaluation of 8 different regression models on their predictive capabilities of disease prevalence, the Cubist model held the strongest results for every disease, achieving considerably low relative mean value errors of 3.849%, 6.077%, 7.656%, 7.866%, and 5.531% towards each of the non-communicable diseases, respectively. Out of the four categories split from 41 input features- Sociodemographic, Economic, Environment/Geographic, and Environmental Justice-Sociodemographic features consistently were the highest contributors. A lightweight model with comparable accuracy to the full feature set ($≤ 0.1%$ difference) was found to require only the 20 most important features from every Cubist model. This study provides insight towards machine learning models' capabilities in enhancing the United States' understanding of domestic epidemiology determinants. © 2024 IEEE.

关键词： Blood pressure

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：