检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Orlichenko, Anton Qu, Gang Zhou, Ziyu Liu, Anqi Deng, Hong-Wen Ding, Zhengming Stephen, Julia M. Wilson, Tony W. Calhoun, Vince D. Wang, Yu-Ping Department of Biomedical Engineering Tulane University New OrleansLA70118 United States Department of Computer Science Tulane University New OrleansLA70118 United States Center for Biomedical Informatics and Genomics Tulane Integrated Institute of Data & Health Sciences Tulane University New OrleansLA70112 United States Institute for Human Neuroscience Boys Town National Research Hospital Boys TownNE United States Mind Research Network AlbuquerqueNM United States Georgia State University Georgia Institute of Technology Emory University AtlantaGA United States

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results:We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data con- ditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds. © 2024, CC BY.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Effects of structure, size and non-magnetic Cu layer thickness on magnetization switching behavior in Ni/Cu/M (M = Ni, Co) cylindrical nanowires

引用

Nano-Structures and Nano-Objects 2023年 35卷

作者： Yamada, Keisuke Kikuchi, Mayu Azizan, Anissa Binti Tsuchiya, Yuuna Ono, Shota Nakatani, Yoshinobu Shima, Mutsuhiro Department of Materials Science and Processing Graduate School of Natural Science and Technology Gifu University Gifu 501-1193 Japan Department of Electrical Electronic and Computer Engineering Gifu University Gifu 501-1193 Japan Institute for Materials Research Tohoku University Sendai 980-8577 Japan Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Chofu 182-8585 Japan

We have investigated the dependence of the magnetization switching behavior on the structure, size and nonmagnetic Cu layer thickness (tCu) in Ni/Cu/M (M = Ni, Co) cylindrical nanowires fabricated by electrodeposition... 详细信息

We have investigated the dependence of the magnetization switching behavior on the structure, size and nonmagnetic Cu layer thickness (t_Cu) in Ni/Cu/M (M = Ni, Co) cylindrical nanowires fabricated by electrodeposition using porous polycarbonate templates. The crystal structure of the Ni nanowires was (111)- or (200)-oriented polycrystalline fcc, while that of Co nanowires was (100)- or (101)-oriented hcp and/or (111)-oriented polycrystalline fcc. When the wire diameter (d) was 50 nm, the squareness ratio (M_r/M_s) for M = Ni, as estimated from the magnetization curve, is almost constant at M_r/M_s∼0.79 (M_r/M_s∼0.64 for M = Co) when t_Cu increases from 0 to 500 nm. When d=100nm, M_r/M_s decreases by 41% from 0.71 to 0.42 with increasing t_Cu from 0 to 700 nm for M = Ni, while M_r/M_s decreases by 25% from 0.53 to 0.40 with varying t_Cu from 0 to 500 nm for M = Co. The threshold Cu layer thickness (t_Cu^th) beyond which M_r/M_s becomes constant at d=100nm is ∼500 nm for M = Ni and ∼300 nm for M = Co. The results are qualitatively consistent with those obtained from our micromagnetic simulation and analysis. The observed difference in the t_Cu^th dependence of M_r/M_s between M = Ni and Co is attributed to the change in the magnetic domain structure of the nanowires as it sensitively varies with the magnetocrystalline anisotropy. On the other hand, the coercive force of the nanowires shows a small t_Cu dependence for both d=50 and 100 nm. These results indicate that the layered structure in the nanowires governs the magnetic domain structure, though the effect on the magnetization switching behavior is indeed relatively small. © 2023 Elsevier B.V.

关键词： Crystal structure Cylindrical nanowire Electrodeposition Magnetization switching Micromagnetics simulation

来源：评论

学校读者我要写书评

暂无评论

An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

引用

Geocarto International 2023年

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

来源：评论

学校读者我要写书评

暂无评论

A New Stock Price Forecasting Method Using Active Deep Learning Approach

引用

Journal of Open Innovation: technology, Market, and Complexity 2022年第2期8卷

作者： Alkhatib, Khalid Khazaleh, Huthaifa Alkhazaleh, Hamzah Ali Alsoud, Anas Ratib Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid 22110 Jordan College of Engineering and IT University of Dubai Academic City Dubai 14143 United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Faculty of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan

Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. This research explores the application of the new approach to predict the adjusted closing price of a specific corporation. A new set of features is used to enhance the possibility of giving more accurate results with fewer losses by creating a six-feature set (that includes High, Low, Volume, Open, HiLo, OpSe), rather than the traditional four-feature set (High, Low, Volume, Open). The study also investigates the effect of data size by using datasets (Apple, ExxonMobil, Tesla, Snapchat) of different sizes to boost open innovation dynamics. The effect of the business sector in terms of the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo, and OpSe) are evaluated according to the model’s outcome, showing fewer losses than the original approach, which utilizes the original feature set. The results show that LSTM-based models improved using the new approach, even though all models showed a comparative result wherein no model showed better results or continuously outperformed other models. Finally, the added new features positively affected the prediction models’ performance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Bi-LSTM CNN deep learning GRU LSTM MLP stock price prediction

来源：评论

学校读者我要写书评

暂无评论

Imaging Intracellular Metabolic Activity of Mammalian Oocytes by Dynamic Full-Field Optical Coherence Microscopy

Imaging Intracellular Metabolic Activity of Mammalian Oocyte...

引用

Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Seweryn Morawiec Patrycjusz Stremplewski Anna Ajduk Brendan Kennedy Maciej Szkulmowski Faculty of Physics Institute of Physics Astronomy and Informatics Nicolaus Copernicus University in Torun Torun Poland BRITElab Harry Perkins Institute of Medical Research QEII Medical Centre Nedlands Western Australia Australia Department of Embryology Faculty of Biology University of Warsaw Warsaw Poland Department of Electrical Electronic & Computer Engineering School of Engineering The University of Western Australia Perth Western Australia Australia Centre for Medical Research The University of Western Australia Crawley Western Australia Australia

ISBN: (纸本)9798350345995

Full-field optical coherence microscopy (FF-OCM) is ideally suited to study living cells as it provides high-resolution cross-section images with no need for fluorescent labelling [1], [2]. The OCM signal is proportional both to the scattering potential of the sample, providing structural information, and to the motion amplitude of the inter-cellular structures. Thus, the metabolic activity information can be extracted from the spatiotemporal analysis of the OCM images. This technique is particularly appealing for embryology to noninvasively assess the development potential of living cells, e.g., oocytes [3].

关键词： Color spaces Image analysis Optical microscopy Phase modulation Spatial resolution Speckle noise

来源：评论

学校读者我要写书评

暂无评论

Enhancing UAV Autonomous Navigation in Indoor Environments Using Reinforcement Learning and Convolutional Neural Networks

Enhancing UAV Autonomous Navigation in Indoor Environments U...

引用

International Symposium on Intelligent Systems and informatics (SISY)

作者： Homayoun Safarpour Motealegh Mahalegi Amirfarhad Farhadi György Molnár Eniko Nagy Obuda University Institute of Mechatronics and Vehicle Engineering Budapest Hungary Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Apaczai Csere Janos Faculty of Humanities Education and Social Sciences Kando Kalman Faculty of Electrical Engineering Budapest Obuda University Hungary Szechenyi University Gyor Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Obuda University Budapest Hungary

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

In recent years, the number of unmanned aerial vehicle (UAV) applications has increased. However, navigating them indoors is still tricky because no GPS signals are available, and the obstacles constantly change. This study exploits the collaboration between convolutional neural networks (CNNs) and reinforcement learning (RL) to overcome these challenges, improving the level of independence of UAVs in indoor settings. We present a new technique known as potential-based reward shaping, which directly incorporates expert knowledge into the reinforcement learning framework. This technique efficiently addresses the problem of sparse rewards that often hinder learning in conventional RL configurations. In addition, we have created a reflective reinforcement learning agent that systematically assesses its actions to prioritize those that result in the most substantial enhancements. We have extensively evaluated the performance of our method with the High-Definition Indoor Navigation (HDIN) dataset to show that our method continuously outperformed current non-introspective RL techniques in terms of decision-making speed and navigation accuracy in this evaluation. The results show that this new CNN-RL combination is feasible and suggests a scalable method for complex indoor navigation problems.

关键词： Indoor navigation Refining Reinforcement learning Autonomous aerial vehicles Indoor environment Convolutional neural networks Intelligent systems informatics Autonomous robots Global Positioning System

来源：评论

学校读者我要写书评

暂无评论

Power of sequential protocols in hidden quantum channel discrimination

arXiv

引用

arXiv 2023年

作者： Sugiura, Sho Dutt, Arkopal Munro, William J. Zeytinoǧlu, Sina Chuang, Isaac L. Physics and Informatics Laboratory Ntt Research Inc. 940 Stewart Dr. SunnyvaleCA94085 United States Laboratory for Nuclear Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Co-Design Center for Quantum Advantage Massachusetts Institute of Technology CambridgeMA02139 United States Ntt Basic Research Laboratories Research Center for Theoretical Quantum Physics 3-1 Morinosato-Wakamiya Kanagawa Atsugi243-0198 Japan Department of Physics Harvard University CambridgeMA02138 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

In many natural and engineered systems, unknown quantum channels act on a subsystem that cannot be directly controlled and measured, but is instead learned through a controllable subsystem that weakly interacts with it. We study quantum channel discrimination (QCD) under these restrictions, which we call hidden system QCD (HQCD). We find that sequential protocols achieve perfect discrimination and saturate the Heisenberg limit. In contrast, depth-1 parallel and multi-shot protocols cannot solve HQCD. This suggests that sequential protocols are superior in experimentally realistic situations. Copyright © 2023, The Authors. All rights reserved.

关键词： Communication channels (information theory)

来源：评论

学校读者我要写书评

暂无评论

Applications of Machine Learning Techniques in Detecting Skin Cancer

Applications of Machine Learning Techniques in Detecting Ski...

引用

International Conference on Contemporary Computing and informatics (IC3I)

作者： Pankaj Kunekar Rounak Vaishnav E. Kalaivani Durgaprasad Gangodkar Chamandeep Kaur Joshuva Arockia Dhanraj Vishwakarma Institute of Technology Pune Department of Medical Oncology Sri Ram Cancer and Super specialty centre Clinical Research Manager Mahatma Gandhi University of medical sciences and technology Jaipur Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science & Information Technology Jazan University Saudi Arabia Department of Mechatronics Engineering Centre for Automation & Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai India

The most lethal kind of skin cancer is called malignant melanoma. Accurate diagnosis of skin malignancies are aided by dermoscopy, a non-invasive high-resolution imaging procedure. Melanoma is a very dangerous kind of skin cancer because of how quickly it spreads and how aggressive it is. For this reason, malignant melanoma maintains its place as one of the tumors with the highest rates of growth across the globe. Second-order texture feature data may be obtained by the GLCM (Grey Level Co-occurrence Matrix) technique. The reference pixel and neighboring pixels are considered by GLCM to ensure an accurate calculation. Grayscale pictures have a decent number of levels, which is straightforwardly relative to the number of segments and columns in the frameworks used to characterize the scale. Relative recurrence is addressed by the grid component P I j | Dx, Dy), where I and j are force values and Dx and Dy are pixel distances. In total, the co-occurrence matrix considers 14 different attributes, including energy, efficiency, latency, correlation, homogeneity, and contrast. Convolutional Neural networks (CNN) and Gaussian Filters (GF) classifiers were then used to sort these vectors. Comparisons of the results these classifiers provide are also made.

关键词： Force Medical treatment High-resolution imaging Melanoma Machine learning Gray-scale Skin

来源：评论

学校读者我要写书评

暂无评论

Expression of Concern for: Applications of Machine Learning Techniques in Detecting Skin Cancer

Expression of Concern for: Applications of Machine Learning ...

引用

International Conference on Contemporary Computing and informatics (IC3I)

ISBN: (数字)9798350398267

ISBN: (纸本)9798350398274

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Block Chain technology to protect Data Access using Intelligent Contracts Mechanism Security Framework for 5G Networks

The Block Chain Technology to protect Data Access using Inte...

引用

International Conference on Contemporary Computing and informatics (IC3I)

作者： Ranjeet Yadav Ritambhara Karthik Kumar Vaigandla G S Pradeep Ghantasala Rajesh Singh Durgaprasad Gangodkar Lecturer in Electronics and Communication Engineering Government Polytechnic Daman Varkund Nani Daman UT Administration of Dadra and Nagar Haveli & Daman and Diu India Department of Electronics and Communication Engineering Jaipur Engineering College and Research centre Jaipur Rajasthan India Department of Electronics and Communication Engineering Balaji Institute of Technology and Science Warangal Telangana India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The introduction of the study primarily emphasises the significance of utilising block chain technologies with the possibility of privacy and security benefits from the 5G Network. One may state that the study’s primary focus is on all the advantages of adopting block chain technology to safeguard everyone’s access to crucial data by utilizing intelligent contracts to enhance the 5G network security model on information security *** literature evaluation for the study focuses primarily on the advantages advantages of utilizing block chain technology advance data security and privacy, as well as their development and growth. The whole study paper has covered both the benefits and drawbacks of employing the block chain technology. The literature study part of this research article has, on the contrary hand, also studied several approaches and tactics for using the blockchain technology facilities. To fully understand the circumstances in this specific case, a poll was undertaken. It was possible for the researchers to get some real-world data in this specific situation by conducting a survey with 51 randomly selected participants.

关键词： Data privacy Privacy 5G mobile communication Data security Information security Network security Blockchains

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：