检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Rohanian, Morteza Hough, Julian Purver, Matthew Cognitive Science Group School of Electronic Engineering and Computer Science Queen Mary University of London Department of Knowledge Technologies Jožef Stefan Institute

This paper is a submission to the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge, which aims to develop methods that can assist in the automated prediction of severity of Alzheimer's Disease from speech data. We focus on acoustic and natural language features for cognitive impairment detection in spontaneous speech in the context of Alzheimer's Disease Diagnosis and the mini-mental state examination (MMSE) score prediction. We proposed a model that obtains unimodal decisions from different LSTMs, one for each modality of text and audio, and then combines them using a gating mechanism for the final prediction. We focused on sequential modelling of text and audio and investigated whether the disfluencies present in individuals' speech relate to the extent of their cognitive impairment. Our results show that the proposed classification and regression schemes obtain very promising results on both development and test sets. This suggests Alzheimer's Disease can be detected successfully with sequence modeling of the speech data of medical sessions. © 2021, CC BY.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Self-Ensembling Semi-Supervised Model for Bone X-ray Images Landmark Detection

Self-Ensembling Semi-Supervised Model for Bone X-ray Images ...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Bai, Tian Liu, Shenyao Wang, Yuzhao Wang, Yu Dong, Dong Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China College of Software Jilin University Changchun China Department of Radiology First Hospital of Jilin University Changchun China

ISBN: (纸本)9781665401265

Bone image landmark detection plays a vital role in orthopedic diseases diagnosis and analysis. As a promising approach, the data-driven convolutional neural network has been widely applied in landmarks detection. However, the model's performance relies heavily on numerous high-quality labeled data, which are difficult to access. A practical solution for this issue in bone images landmark detection has not been provided, so we propose a framework of semi-supervised bone images landmark detection encouraged by the recent success of the self-ensembling method. The student and teacher networks share the same structure in our framework, where unlabeled data are utilized by enforcing the prediction consistency of two networks under different perturbations to enhance the network's performance. The two networks use a mutual learning approach to improve the efficiency of using unlabeled data. In addition, we also propose a transformative perturbation scheme to prevent model overfitting during training and enhance the generalization capability of the model on unseen data. Experimental results on an in-house hip dataset collected from the actual process of clinical diagnosis and a publicly available dataset demonstrate that our framework has excellent performance compared with other semi-supervised methods. © 2021 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Synthetic Instance Segmentation from Semantic Image Segmentation Masks

arXiv

引用

arXiv 2023年

作者： Shen, Yuchen Zhang, Dong Zhang, Zhao Fu, Liyong Ye, Qiaolin College of Information Science and Technology & Artificial Intelligence Nanjing Forestry University Jiangsu Nanjing210037 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230009 China College of Information Science and Technology Nanjing Forestry University Jiangsu Nanjing210037 China Institute of Forest Resource Information Techniques Chinese Academy of Forestry Beijing100091 China

In recent years, instance segmentation has garnered significant attention across various applications. However, training a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations. In contrast, weakly-supervised instance segmentation methods, such as those using image-level class labels or point labels, often struggle to satisfy the accuracy and recall requirements of practical scenarios. In this paper, we propose a novel paradigm called Synthetic Instance Segmentation (SISeg). SISeg achieves instance segmentation results by leveraging image masks generated by existing semantic segmentation models, and it is highly efficient as we do not require additional training for semantic segmentation or the use of instance-level image annotations. In other words, the proposed model does not need extra manpower or higher computational expenses. Specifically, we first obtain a semantic segmentation mask of the input image via an existent semantic segmentation model. Then, we calculate a displacement field vector for each pixel based on the segmentation mask, which can indicate representations belonging to the same class but different instances, i.e., obtaining the instance-level object information. Finally, the instance segmentation results are refined by a learnable category-agnostic object boundary branch. Extensive experimental results on two challenging datasets highlight the effectiveness of SISeg in achieving competitive results when compared to state-of-the-art methods, especially fully-supervised methods. The code will be released at: SISeg Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

B-Spine: Learning B-Spline Curve Representation for Robust and Interpretable Spinal Curvature Estimation

arXiv

引用

arXiv 2023年

作者： Wang, Hao Song, Qiang Yin, Ruofeng Ma, Rui Yu, Yizhou Chang, Yi School of Artificial Intelligence Jilin University Changchun130012 China China-Japan Union Hospital Jilin University Changchun130031 China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE China Department of Computer Science University of Hong Kong Pokfulam Hong Kong

Spinal curvature estimation is important to the diagnosis and treatment of the scoliosis. Existing methods face several issues such as the need of expensive annotations on the vertebral landmarks and being sensitive to the image quality. It is challenging to achieve robust estimation and obtain interpretable results, especially for low-quality images which are blurry and hazy. In this paper, we propose B-Spine, a novel deep learning pipeline to learn B-spline curve representation of the spine and estimate the Cobb angles for spinal curvature estimation from low-quality X-ray images. Given a low-quality input, a novel SegRefine network which employs the unpaired image-to-image translation is proposed to generate a high quality spine mask from the initial segmentation result. Next, a novel mask-based B-spline prediction model is proposed to predict the B-spline curve for the spine centerline. Finally, the Cobb angles are estimated by a hybrid approach which combines the curve slope analysis and a curve-based regression model. We conduct quantitative and qualitative comparisons with the representative and SOTA learning-based methods on the public AASCE2019 dataset and our new proposed CJUH-JLU dataset which contains more challenging low-quality images. The superior performance on both datasets shows our method can achieve both robustness and interpretability for spinal curvature estimation.. Copyright © 2023, The Authors. All rights reserved.

关键词： Curve fitting

来源：评论

学校读者我要写书评

暂无评论

Alzheimer's dementia recognition using acoustic, lexical, disfluency and speech pause features robust to noisy inputs

arXiv

引用

arXiv 2021年

We present two multimodal fusion-based deep learning models that consume ASR transcribed speech and acoustic data simultaneously to classify whether a speaker in a structured diagnostic task has Alzheimer's Disease and to what degree, evaluating the ADReSSo challenge 2021 data. Our best model, a BiLSTM with highway layers using words, word probabilities, disfluency features, pause information, and a variety of acoustic features, achieves an accuracy of 84% and RSME error prediction of 4.26 on MMSE cognitive scores. While predicting cognitive decline is more challenging, our models show improvement using the multimodal approach and word probabilities, disfluency, and pause information over word-only models. We show considerable gains for AD classification using multimodal fusion and gating, which can effectively deal with noisy inputs from acoustic features and ASR hypotheses. © 2021, CC BY.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Exploring advanced materials: Harnessing the synergy of inverse gas chromatography and artificial vision intelligence

引用

TrAC - Trends in Analytical Chemistry 2024年 173卷

作者： Basivi, Praveen Kumar Hamieh, Tayssir Kakani, Vijay Pasupuleti, Visweswara Rao Sasikala, G. Heo, Sung Min Pasupuleti, Kedhareswara Sairam Kim, Moon-Deock Munagapati, Venkata Subbaiah Siva Kumar, Nadavala Wen, Jet-Chau Kim, Chang Woo Pukyong National University Industry-University Cooperation Foundation Pukyong National University Busan48513 Korea Republic of Faculty of Science and Engineering Maastricht University P.O. Box 616 MD Maastricht6200 Netherlands Faculty of Sciences Lebanese University Hadath Lebanon Department of Integrated System Engineering INHA University 100 Inha-ro Namgu Incheon22212 Korea Republic of Center for International Relations and Research Collaborations and School of Applied Sciences REVA University Rukmini Knowledge Park Kattigenahalli Yelahanka Karnataka Bangalore560064 India School of Computer Sciences and Applications REVA University Kattigenahalli Rukmini Knowledge Park Bangalore560064 India Department of Smart Green Technology Engineering Pukyong National University Busan48513 Korea Republic of Chungnam National University 99 Daehak-ro Yuseong-gu Daejeon34134 Korea Republic of National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Department of Chemical Engineering College of Engineering King Saud University P.O. Box - 800 Riyadh11421 Saudi Arabia Department of Nanotechnology Engineering College of Engineering Pukyong National University Busan48513 Korea Republic of

Inverse gas chromatography (IGC) has emerged as a highly sensitive, adaptable, and effective technology for material analysis. Through employing thermochemical approaches, IGC provides crucial insight into physicochemical information of materials such as dispersive surface free energy, Gibbs surface energy components and Guttamann Lewis acid-base parameters. In this comprehensive review, we delve into the historical background, instrumentation, and diverse applications of IGC. Researchers and practitioners will find valuable information on the selection and description of numerous models used in IGC experiments. The applications of IGC span various domains, including polymers, medicines, minerals, surfactants, and nanomaterials. Furthermore, IGC facilitates the measurement of important parameters such as sorption enthalpy and entropy, surface energy components (dispersive and specific), co/adhesion work, glass transition temperature, surface heterogeneity, miscibility, solubility parameters, and specific surface area. These insights contribute to a deeper understanding of material behavior and aid in the design and optimization of advanced materials. Moreover, the integration of computer vision and image processing techniques with IGC has enhanced our understanding of materials intricate surface texture, roughness, and related properties. This convergence of IGC with computer vision and artificial intelligence (AI) presents exciting opportunities for future exploration of chemical materials, opening new avenues for research and discovery. This paper not only provides a comprehensive overview of IGC, its techniques, and applications but also highlights the synergistic potential of combining IGC with AI and computer vision. The informative content and insights presented here will benefit researchers, scientists, and professionals in the field of advanced materials, enabling them to leverage IGC and AI for innovative materials discovery and development. © 2024 Elsevier

关键词： Interfacial energy

来源：评论

学校读者我要写书评

暂无评论

Generative AI for Advanced UAV Networking

引用

IEEE Network 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Du, Hongyang Kang, Jiawen Wu, Jing Sun, Sumei Zhang, Ping Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong999077 Hong Kong Guangdong University of Technology School of Automation Guangzhou510006 China Agency for Science Technology and Research Institute for Infocomm Research Singapore639798 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions. © 2024 IEEE.

关键词： Aircraft communication

来源：评论

学校读者我要写书评

暂无评论

Application of Data science on Stock Forecasting and Evaluation Using Artificial Neural Network

Application of Data Science on Stock Forecasting and Evaluat...

引用

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

作者： Sornalakshmi R. R. Poornam Sai Goddali Prinkle Vohra Mohammed Asim Varsha Mittal Isarar Ahamad Saveetha School of Law Saveetha Institute of Medical and Technical Sciences Chennai TN India Deptment of Mechanical Engineering Rajiv Gandhi University of Knowledge Technologies Andhra Pradesh India School of Management Graphic Era Hill University Haldwani Campus India Department of Electrical Engineering Integral University Lucknow India Deptt. of Computer Science & Engg. Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Stock exchanges are regarded as significant players in the financial sector in many countries. Most stockbrokers that execute trades use technical, fundamental, or time series analysis to try and predict stock values in order to advise clients. However, these strategies usually don't guarantee large profits because they go by trends rather than the most likely price. Thus, it is imperative to look into more sophisticated prediction methods. Although stock market prediction is a common utilization of artificial neural networks (ANN), stock market forecasting remains challenging due to the multitude of known and unknown factors that impact it. In this work, we proposed an ANN-based method for daily stock price forecasting. The results of statistical forecasting are contrasted with the ANN forecast. Using MAE, MSE, and RMSE, one can obtain the predicting capability of these two methods. The findings demonstrate that ANNs are capable of accurately predicting stock market prices when they are trained with enough data, appropriate inputs, and a suitable design. As a result, ANN offers a superior substitute method for daily stock market price forecast.

关键词： Measurement Training Statistical analysis Time series analysis Artificial neural networks Predictive models Data science Data models Stock markets Forecasting

来源：评论

学校读者我要写书评

暂无评论

Unboxing the black-box of deep learning based reconstruction of undersampled MRIs 4

Unboxing the black-box of deep learning based reconstruction...

引用

4th Italian Workshop on Explainable Artificial Intelligence, XAI.it 2023

作者： Chatterjee, Soumick Das, Arnab Khatun, Rupali Nürnberger, Andreas Genomics Research Centre Human Technopole Milan Italy Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany German Research Centre for Artificial Intelligence Kaiserslautern Germany Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany Comprehensive Cancer Centre Erlangen-EMN Erlangen Germany Centre for Behavioural Brain Sciences Magdeburg Germany

Deep learning has emerged as a very important area of research and has shown immense potential in solving different kinds of problem, including in the medical field. For tasks like undersampled MRI reconstruction - the process of speeding up MRI acquisition with the help of undersampling, deep learning has shown its dominance over the years. But one of the major problems with deep learning is trust: Complex reasonings done by these models appear black-box to the users. Therefore, to build trust and better acceptability, it is important to open up this black-box nature of these models. For classification models, several approaches have been proposed. Nevertheless, for models dealing with inverse problems, like the reconstruction of the undersampled MRIs, it is more challenging as the output of the model has the same number of output pixels as the input, making the interpretability of such models more complex. This research explores different methods to understand the working mechanism of a deep learning model for the task of undersampled MRI reconstruction. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

Machine learning in diagnosis of coronary artery disease

引用

Chinese Medical Journal 2021年第4期134卷 401-403页

作者： Hao Ling Zi-Yuan Guo Un-Lin Tan Ren-Chu Guan Jing-Bo Chen Chun-Li Department of Cardiology the Second Hospital of Jilin UniversityChangchunJilin 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and TechnologyJilin UniversityChangchunJilin 130012China

Cardiovascular diseases continue to be the foremost cause of death among both males and females worldwide.[1]Coronary artery disease(CAD)represents a major subtype of cardiovascular *** evidence has highlighted a worldwide increase in incidence of CAD clinical manifestations regardless of geographical region.[2]Despite a steady decline in CAD mortality in western countries over the past few decades,approximately one-third of the population suffering from CAD will succumb to the *** and accurate diagnostic decisions are crucial for patients with CAD and aid in guidance for effective treatment.

关键词： CAD diagnosis continue

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：