检索结果-内蒙古大学图书馆

18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Brügge, Nele Sophie Korda, Alexandra Borgwardt, Stefan Andreou, Christina Giannakakis, Giorgos Handels, Heinz German Research Center for Artificial Intelligence AI in Medical Image and Signal Processing Lübeck Germany Translational Psychiatry Department of Psychiatry and Psychotherapy University of Luebeck Lübeck23562 Germany Heraklion Greece Department of Electronic Engineering Hellenic Mediterranean University Chania Greece Institute of Agri-food and Life Sciences University Research and Innovation Center Hellenic Mediterranean University Heraklion Greece Institute of Medical Informatics University of Luebeck Lübeck Germany

Stress detection is a complex challenge with implications for health and well-being. It often relies on sensors recording biomarkers and biosignals, which can be uncomfortable and alter behaviour. Video-based facial feature analysis offers a noninvasive alternative. This study explores video-level stress detection using top-k Multiple Instance Learning applied to medical videos. The approach is motivated by the assumption that subjects partly show normal behaviour while performing stressful experimental tasks. Our contributions include a tailored temporal feature network and optimised data utilisation by additionally incorporating bottom-k snippets. Leave-five-subjects-out stress detection results of 95.46% accuracy and 95.49% F1 score demonstrate the potential of our approach, outperforming the baseline methods. Additionally, through multiple instance learning, it is possible to show which temporal video segments the network pays particular attention to. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation

arXiv

引用

arXiv 2025年

作者： Solé, Àlex Mosella-Montoro, Albert Cardona, Joan Gómez-Coca, Silvia Aravena, Daniel Ruiz, Eliseo Ruiz-Hidalgo, Javier Image Processing Group Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Spain Inorganic and Organic Chemistry Department Institute of Theoretical and Computational Chemistry Universitat de Barcelona Barcelona Spain Materials Chemistry Department Faculty of Chemistry and Biology Universidad de Santiago de Chile Santiago Chile

In the diffraction resolution of crystal structures, the thermal ellipsoids are a critical parameter that is usually more difficult to determine than atomic positions. These ellipsoids are quantified through the Anisotropic Displacement Parameters (ADPs), which provide critical insights into atomic vibrations within crystalline structures. ADPs reflect the thermal behaviour and structural properties of crystal structures. However, traditional methods to compute ADPs are computationally intensive. This paper presents CartNet, a novel graph neural network (GNN) architecture designed to predict properties of crystal structures efficiently by encoding the atomic structural geometry to the cartesian axes and the temperature of the crystal structure. Additionally, CartNet employs a neighbour equalization technique for message passing to help emphasise the covalent and contact interactions, and a novel Cholesky-based head to ensure valid ADP predictions. Furthermore, a rotational SO(3) data augmentation technique has been proposed during the training phase to generalize unseen rotations. To corroborate such procedure, an ADP dataset with over 200,000 experimental crystal structures from the Cambridge Structural Database (CSD) has been curated. The model significantly reduces computational costs and outperforms existing previously resported methods in ADP prediction by 10.87%, while demonstrating a 34.77% improvement over the tested theoretical computation methods. Moreover, we have employed CartNet for other already known datasets that included different material properties, such as formation energy, band gap, total energy, energy above the convex hull, bulk moduli, and shear moduli. The proposed architecture outperformed previously reported methods by 7.71% in the Jarvis Dataset and 13.16% in the Materials Project Dataset, proving CarNet’s capability to achieve state-of-the-art results in several tasks. Project website with online demo available at: https://***/

关键词： Encoding (symbols)

来源：评论

学校读者我要写书评

暂无评论

A Comparison of Approximate Analytic and Neural Network Solutions for Effective Mass Schrodinger Equation With Yukawa's Potential 2

A Comparison of Approximate Analytic and Neural Network Solu...

引用

2nd IEEE International Conference on Artificial intelligence in Everything, AIE 2022

作者： Fotso Kamsu, Brigitte Vubangsi, Mercel Tchoffo, M. Al-Turjman, Fadi University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Faculty of Science Cameroon Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Htttc Bambili Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

We model a compositionally graded heterostructure using the effective mass approach with a screened Coulomb potential. Approximate analytical solutions to the model are compared with a Neural Network output and good agreement is achieved, paving the way for Artificial intelligence modeling of systems with graded composition. © 2022 IEEE.

关键词： Grading

来源：评论

学校读者我要写书评

暂无评论

RETRACTED ARTICLE: A comparative recognition research on excretory organism in medical applications using neural networks

引用

Multimedia Tools and Applications 2023年 1-2页

作者： K, Vidhya R, Krishna Priya Vidhyalakshmi, M. Ramesh, S. M. L, Bharathi Kshirsagar, Pravin R. Rajaram, A. Tirth, Vineet Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Engineering - Electrical Section University of Technology and Applied Sciences Shinas Campus Sultanate of Oman Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Signal and Image Processing With Computational Intelligence Saveetha School of Engineering Chennai India Department of Electricals and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India Department of Mechanical Engineering College of Engineering King Khalid University Abha Kingdom of Saudi Arabia

来源：评论

学校读者我要写书评

暂无评论

Automatic Detection of Microaneurysms in OCT images Using Bag of Features

arXiv

引用

arXiv 2022年

作者： Nasab, Elahe Sadat Kazemi Almasi, Ramin Shoushtarian, Bijan Golkar, Ehsan Rabbani, Hossein Department of Artificial Intelligence Faculty of Computer Engineering University of Isfahan Isfahan Iran Medical Image & Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran Department of Computer Architecture Faculty of Computer Engineering University of Isfahan Isfahan Iran

Diabetic Retinopathy (DR) caused by diabetes occurs as a result of changes in the retinal vessels and causes visual impairment. Microaneurysms (MAs) are the early clinical signs of DR, whose timely diagnosis can help detecting DR in the early stages of its development. It has been observed that MAs are more common in the inner retinal layers compared to the outer retinal layers in eyes suffering from DR. Optical Coherence Tomography (OCT) is a noninvasive imaging technique that provides a cross-sectional view of the retina and it has been used in recent years to diagnose many eye diseases. As a result, in this paper has attempted to identify areas with MA from normal areas of the retina using OCT images. This work is done using the dataset collected from FA and OCT images of 20 patients with DR. In this regard, firstly Fluorescein Angiography (FA) and OCT images were registered. Then the MA and normal areas were separated and the features of each of these areas were extracted using the Bag of Features (BOF) approach with Speeded-Up Robust Feature (SURF) descriptor. Finally, the classification process was performed using a multilayer perceptron network. For each of the criteria of accuracy, sensitivity, specificity, and precision, the obtained results were 96.33%, 97.33%, 95.4%, and 95.28%, respectively. Utilizing OCT images to detect MAs automatically is a new idea and the results obtained as preliminary research in this field are promising. © 2022, CC BY-NC-SA.

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification

引用

Science China(Technological Sciences) 2021年第9期64卷 1863-1871页

作者： ZHANG Jin FENG Fan HAN TianYi DUAN Feng SUN Zhe CAIAFA Cesar F. SOLÉ-CASALS Jordi College of Computer Science Nankai UniversityTianjin 300350China Department of Artificial Intelligence Nankai UniversityTianjin 300350China Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKEN351-0198 SaitamaJapan Instituto Argentino de Radioastronomía-CCT La Plata CONICET/CIC-PBA/UNLP1894 V.ElisaArgentina Department of Psychiatry University of CambridgeCambridge CB20SZUnited Kingdom Data and Signal Processing Group University of Vic-Central University of Catalonia08500 VicCataloniaSpain

Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.

关键词： gifted children identification morphometric features tensor completion feature selection

来源：评论

学校读者我要写书评

暂无评论

From early biological models to CNNs: Do they look where humans look? 25

From early biological models to CNNs: Do they look where hum...

引用

25th International Conference on Pattern Recognition, ICPR 2020

作者： Cadoni, Marinella Iole Lagorio, Andrea Grosso, Enrico Jia Huei, Tan Chee Seng, Chan Computer Vision Laboratory University of Sassari Sassari Italy Center of Image and Signal Processing Department of Artificial Intelligence Faculty of Computer Science and Information Technology University of Malaya Malaysia

ISBN: (纸本)9781728188089

Early hierarchical computational visual models as well as recent deep neural networks have been inspired by the functioning of the primate visual cortex system. Although much effort has been made to dissect neural networks to visualize the features they learn at the individual units, the scope of the visualizations has been limited to a categorization of the features in terms of their semantic level. Considering the ability humans have to select high semantic level regions of a scene, the question whether neural networks can match this ability, and if similarity with humans attention is correlated with neural networks performance naturally arise. To address this question we propose a pipeline to select and compare sets of feature points that maximally activate individual networks units to human fixations. We extract features from a variety of neural networks, from early hierarchical models such as HMAX up to recent deep convolutional neural netwoks such as Densnet, to compare them to human fixations. Experiments over the ETD database show that human fixations correlate with CNNs features from deep layers significantly better than with random sets of points, while they do not with features extracted from the first layers of CNNs, nor with the HMAX features, which seem to have low semantic level compared with the features that respond to the automatically learned filters of CNNs. It also turns out that there is a correlation between CNN's human similarity and classification performance. © 2020 IEEE

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Learning Assisted Approach for Electronic and Optical Characterization of Tin-Doped Titanium Oxide 2

A Learning Assisted Approach for Electronic and Optical Char...

引用

2nd IEEE International Conference on Artificial intelligence in Everything, AIE 2022

作者： Vubangsi, Mercel Nforna, Edwin Akongnwi Kamsu, Brigitte Fotso Tchoffo, M. Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon Htttc Bambili University of Bamenda Materials Science Research Unit Department of Fundamental Sciences Cameroon Faculty of Science University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin Kyrenia10 Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we evaluate the effect of variation of mole fraction of Tin in doped titanium oxide on band gap. To achieve this, we resort to Matminer's "dielectric_constant"dataset and apply an algorithm to featurize the chemical formulae of the compounds in the dataset. Using k-fold cross-validation of random forest, we obtain a Machine learning model with R2 value of 0.81 MAE=0.45. Applying our model to predict the band gap of doped Titanium oxide over a range of 6 compositions, we find that the 50% doping level has a remarkably lower band gap than all other compositions which makes it a more suitable candidate for optical applications such as in solar cells. © 2022 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

A comparative recognition research on excretory organism in medical applications using neural networks

引用

Multimedia Tools and Applications 2023年

作者： K, Vidhya R, Krishna Priya Vidhyalakshmi, M. Ramesh, S. M. L, Bharathi Kshirsagar, Pravin R. Rajaram, A. Tirth, Vineet Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Engineering - Electrical Section University of Technology and Applied Sciences Shinas Campus Oman Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Signal and Image Processing With Computational Intelligence Saveetha School of Engineering Tamil Nadu Chennai600124 India Department of Electricals and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

In the current generation, most individuals are facing many health problems even in the elderly stages and one of the severe touch is caused by to digestive system. Usually, the problems in the digestive system transpire due to improper innards that are present inside it. Even though many experts have described effective solutions to overcome such problems no proper outcomes are achieved. Therefore, the proposed method builds an appropriate device that detects the harmful contents in the entire digestive system which records and stores the data for a continuous period. Additionally, the designed device is incorporated with the neural network using five defined layers where the data is divided into mini segments. This type of data segmentation of input images will reduce the amount of loss in the system thus making the system to be operated at reduced cost. Further equal weights are allocated in the hidden layer using reduced control parameters thereby achieving concentrated attack at several periods. To analyze the effectiveness of neural networks parametric cases are performed and the results prove to be very effective in comparison cases for about 68 percentage. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Medical applications

来源：评论

学校读者我要写书评

暂无评论

AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR

JACC: Advances

引用

JACC: Advances 2024年第10期3卷 101249-101249页

作者： Han, Donghee Shanbhag, Aakash Miller, Robert JH. Kwok, Nicholas Waechter, Parker Builoff, Valerie Newby, David E. Dey, Damini Berman, Daniel S. Slomka, Piotr Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA United States Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Cardiac Sciences University of Calgary Calgary AB Canada British Heart Foundation Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom

Background: Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR). Objectives: The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR. Methods: We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR. Results: A total of 316 patients (age: 57.1 ± 16.7 years, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r = 0.84, P < 0.001), with no significant differences (136.5 ± 55.3 g vs 139.6 ± 56.9 g, P = 0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to CMR, measured LV mass was higher with AI (136.5 ± 55.3 g vs 127.1 ± 53.1 g, P < 0.001). There was an excellent correlation between AI and CMR (r = 0.85, P < 0.001), with a small bias (−9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and CMR or AI and CMR. Conclusions: The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR. © 2024 The Authors

关键词： artificial intelligence cardiac magnetic resonance imaging computed tomography myocardial mass

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：