检索结果-内蒙古大学图书馆

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to "A federated learning approach for classifying chest diseases from chest X-ray images" [Biomed. Signal Process. Control 100(Part A) (2025) 107107] (Biomedical Signal Processing and Control (2025) 100(PA), (S1746809424011650), (10.1016/***.2024.107107))

引用

Biomedical Signal Processing and Control 2025年 106卷

作者： Satapathy, Sandeep Kumar Cho, Sung-Bae Mishra, Shruti Sah, Sweeti Kamaldeep Mohanty, Sachi Nandan Dinesh, J. Department of Computer Science Yonsei University 50 Yonsei-ro Sudaemoon-gu Seoul03722 Korea Republic of Centre for Advanced Data Science Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computer Engineering National Institute of Technology Haryana Kurukshetra136119 India Department of Media Engineering National Institute of Technical Teachers Training and Research Chandigarh160019 India VIT-AP University Andhra Pradesh Amaravati522237 India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The authors regret the omission of Mr. Dinesh J, School of computer science and Engineering, Vellore institute of Technology, Chennai, who initially undertook this work as a capstone project under the supervision of Dr. Sandeep Kumar Satapathy. His contributions in the early stages were instrumental in shaping the direction of this study. We sincerely acknowledge his efforts and appreciate his role in the initial development of this work. The correct order of authors list have been updated as above. The authors would like to apologise for any inconvenience caused. © 2024 Elsevier Ltd

关键词：

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Zero Trust Architecture: Enhancing Cyber-Security Resilience

AI-Driven Zero Trust Architecture: Enhancing Cyber-Security ...

引用

International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： K. Chokkanathan S.M. Karpagavalli G. Priyanka K. Vanitha K. Anitha P. Shenbagavalli Department of Artificial Intelligence Madanapalle Institute of Technology & Science Department of M.Tech Computer Science and Engineering Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Information Technology CSI College of Engineering Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

In the ever-changing field of cybersecurity, conventional defenses that rely on boundaries are no longer adequate to safeguard against sophisticated and persistent assaults. The Zero Trust Architecture (ZTA) paradigm, powered by AI, provides a strong answer by removing the concept of inherent trust within a network and demanding ongoing verification for each access request. This article examines the combination of artificial intelligence (AI) with Zero Trust principles to improve the resilience of cyber-security. This study demonstrates the enhanced accuracy, sensitivity, and specificity of AI-driven techniques in comparison to conventional security models. The findings highlight the significance of utilizing AI-driven Zero Trust Architecture (ZTA) to protect contemporary digital infrastructures. It provides robust protection against emerging cyber threats as well as addressing challenges related to data privacy and system integration. The paper also explores potential areas for future study and the practical consequences of implementing AI-driven Zero Trust Architecture (ZTA) in different organizational settings.

关键词： data privacy Accuracy Training data computer architecture Transforms Zero Trust Time factors computer security Artificial intelligence Resilience

来源：评论

学校读者我要写书评

暂无评论

A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT dataset 26

A Horizontal Federated Learning Approach to IoT Malware Traf...

引用

26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Do, Phuc Hao Le, Tran Duc Vishnevsky, Vladimir Berezkin, Aleksandr Kirichek, Ruslan The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia University of Science and Technology The University of Danang Da Nang Viet Nam The University of Danang University of Science and Technology Information Technology Faculty Danang Viet Nam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Bonch Bruevich Saint Petersburg State University of Telecommunications Department of Programming Engineering and Computer Science Russia

ISBN: (纸本)9791188428120

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy. © 2024 Global IT Research institute - GIRI.

关键词： Malware

来源：评论

学校读者我要写书评

暂无评论

Surface Plasmon Enhanced Light-Emitting Diode with Hybrid Microcavity Spacing

Surface Plasmon Enhanced Light-Emitting Diode with Hybrid Mi...

引用

Devices for Integrated Circuit (DevIC)

作者： Sheela D Bhavani N. P.G Prameeladevi C Sarada Devi CH Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Velammal Institute of Technology Thiruvallur Tamil Nadu India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

The light emission of LED structures can be enhanced by coupling to surface plasmons by using a periodic pattern formed on the top semitransparent metal layer resulting in microcavities. The improved light emission results from trapping of pump photons within the microcavity. The spacing between the microcavities has a strong impact on the light emission characteristics. In this paper, the LED structure with hybrid pattern of spacing interval is developed. The spacing between the silver stripes is same on both the ends in the top layer whereas the spacing between the middle two stripes are kept wider. The electromagnetic field distribution for TE and TM modes is investigated for this approach and compared with the structure of uniform spacing interval. Results show that there is an improvement in the field distribution in TE mode in the structure with hybrid spacing interval when compared with the structure with uniform interval.

关键词： Couplings Integrated circuits Silver Spontaneous emission Light emitting diodes Microcavities Surface plasmons Time-domain analysis Finite difference methods Periodic structures

来源：评论

学校读者我要写书评

暂无评论

Linear Tapered Wavelength Division Multiplexing (WDM) Phasor Array to Improve Coupling Efficiency

Linear Tapered Wavelength Division Multiplexing (WDM) Phasor...

引用

Devices for Integrated Circuit (DevIC)

作者： Sheela D Bhavani N. P.G Prameeladevi C Sarada Devi CH Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Velammal Institute of Technology Tamil Nadu India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

The rapid growth in demand for high-capacity telecommunication links, and the speed limitation of single-wavelength links, has resulted in an extraordinary increase in the use of wavelength-division multiplexing (WDM) in advanced lightwave networks. The existing research focuses on waveguide tapering, with limited attention given to tapering effects in WDM phasor arrays. This study introduces a tapered WDM phasor array and investigates how the tapering ratio influences the output field. A WDM phasor array with linear tapering is designed with uniform separation. The performance is analyzed across various tapering ratios and compared with the non-tapered structure. The ouput is analyzed in terms of optical field, electric field and effective refractive index. Results show that lower tapering ratios improve output compared to non-tapered designs, while higher tapering ratios lead to reduced output field strengths.

关键词： Couplings Integrated circuits Refractive index Wavelength division multiplexing Optical variables control Optical coupling Communications technology Optical refraction Electric fields Optical waveguides

来源：评论

学校读者我要写书评

暂无评论

Approximating Positive Homogeneous Functions with Scale Invariant Neural networks

arXiv

引用

arXiv 2023年

作者： Bamberger, Stefan Heckel, Reinhard Krahmer, Felix Department of Mathematics Technical University of Munich Holsten Systems GmbH Germany Department of Computer Engineering Technical University of Munich Munich Center for Machine Learning Germany Department of Mathematics Munich Data Science Institute Technical University of Munich Munich Center for Machine Learning Germany

We investigate to what extent it is possible to solve linear inverse problems with ReLu networks. Due to the scaling invariance arising from the linearity, an optimal reconstruction function f for such a problem is positive homogeneous, i.e., satisfies f(λx) = λf(x) for all non-negative λ. In a ReLu network, this condition translates to considering networks without bias terms. We first consider recovery of sparse vectors from few linear measurements. We prove that ReLu- networks with only one hidden layer cannot even recover 1-sparse vectors, not even approximately, and regardless of the width of the network. However, with two hidden layers, approximate recovery with arbitrary precision and arbitrary sparsity level s is possible in a stable way. We then extend our results to a wider class of recovery problems including low-rank matrix recovery and phase retrieval. Furthermore, we also consider the approximation of general positive homogeneous functions with neural networks. Extending previous work, we establish new results explaining under which conditions such functions can be approximated with neural networks. Our results also shed some light on the seeming contradiction between previous works showing that neural networks for inverse problems typically have very large Lipschitz constants, but still perform very well also for adversarial noise. Namely, the error bounds in our expressivity results include a combination of a small constant term and a term that is linear in the noise level, indicating that robustness issues may occur only for very small noise *** Codes 41A30, 68T07 Copyright © 2023, The Authors. All rights reserved.

关键词： Recovery

来源：评论

学校读者我要写书评

暂无评论

Investigation of Automatic Part-of-Speech Tagging using CRF, HMM and LSTM on Misspelled and Edited Texts 5

Investigation of Automatic Part-of-Speech Tagging using CRF,...

引用

5th Artificial Intelligence and Cloud Computing Conference, AICCC 2022

作者： Aydinov, Farhad Huseynov, Igbal Sayadzada, Sofiya Rustamov, Samir Data Science University of Glasgow United Kingdom Computational Linguistics and Language Technologies Charles University Prague Czech Republic Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark School of Information Technology and Engineering ADA University Baku Azerbaijan Institute of Control Systems BakuVarna Bulgaria Azerbaijan

ISBN: (纸本)9781450398749

Part-of-speech tagging is the process of assigning words in a given text to appropriate parts-of speech in order to reduce the disambiguation which may arise depending on the contextual usage of the words. In this paper, the problem of word sense disambiguation in Azerbaijani language is addressed by applying part of speech tagging on two varying data corpora, misspelled, and edited (clean) text using 3 different machine learning algorithms: Hidden Markov Model, Long Short-Term Memory, and Conditional Random Fields. The comparative analysis on the outcomes of the mentioned algorithms and their accuracy scores were analysed in the paper. The misspelled dataset for the experiments is provided by Unibank from their chatbot dialogues while the clean textual data was retrieved from the books and newspapers in Azerbaijani. The experiments showed that the Bidirectional LSTM has the highest accuracy scores for both edited (98.2%) and noisy (96.2%) data corpora. Suggested models can be used in the application of algorithms focuses on part of speech tags and syntactic structure of Azerbaijani language which is an agglutinative language belonging to Turkic languages family, thus enabling the research to be further investigated in other agglutinative languages with similar grammatical structure. © 2022 ACM.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

Human-computer Interaction in Augmented Reality: Applications and Challenges

Human-Computer Interaction in Augmented Reality: Application...

引用

Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Eman Ahmad Shudayfat Raed Alazaidah Mohammad Aljaidi Marina Yusoff Maram Y. Al-Safarini Omar Alidmat Department of Creative media Luminus Technical University Amman Jordan Department of Data Science and AI Zarqa University Zarqa Jordan Departmemt of Computer Science Zarqa University Zarqa Jordan Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA (UiTM) Shah Alam-Selangor Malaysia Department of Computer Science Zarqa University Zarqa Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

a technique called Augmented Reality (AR) superimposes digital data on the physical world. This technology has the power to completely change how humans communicate with computers. Nonetheless, a number of issues still need to be resolved before AR user interfaces can be made to feel intuitive, natural, and effective. The applications and prospects of Human-computer Interaction (HCI) in AR are covered in this paper. Compared to conventional user interfaces, the authors contend that AR has the potential to produce more engaging and natural user interfaces. They also make the point that in order to fully grasp this potential, AR designers must create new interaction strategies and design tenets.

关键词： Human computer interaction computers Electric potential Artificial intelligence Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Advanced Deep Learning Model for Enhanced Kidney Disease Detection

Advanced Deep Learning Model for Enhanced Kidney Disease Det...

引用

data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： K. Anita Davamani Dhanya D S. Pushparani Vidhya R Geetha Mohan Jani Anbarasi L Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Artificial Intelligence and Data Science Mar Ephraem College of Engineering and Technology Elavuvilai India Department of CSE Meenakshi College of Engineering Chennai India Department of Information Technology Agni College of Technology Chennai India Computer Science Institute of Applied Technology Abu Dhabi School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Automated Chronic Kidney Disease (CKD) is a swiftly growing non-communicable condition that contributes to a substantial number of deaths and illnesses worldwide. Affecting over 10% of the global population, CKD results in millions of fatalities each year. In this study, an advanced deep learning model for kidney disease detection is proposed that combines convolutional and involution layers to improve classification accuracy. The model processes input images by first applying a 2D convolution with 32 filters of size 3x3, followed by ReLU activation to extract key features. Max-pooling layers are then used to reduce spatial dimensions while retaining crucial information. The convolutional layers are sequentially stacked with larger filter sizes, resulting in progressively richer feature representations. To enhance feature extraction, the model integrates custom involution layers, which extend traditional convolution operations and are particularly effective for processing large-scale image data. These involution layers are applied after the convolutional steps, utilizing 128 and 256 filters of size 3 × 3, followed by max-pooling to further refine the extracted features. A comparative analysis was carried out between the proposed custom Convolutional Neural Network (CNN) model and previous research works, utilizing metrics such as Recall, Precision, F1-Score, Accuracy, Loss, Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves. The results indicate that the proposed model achieves superior classification prediction performance, with an accuracy of 99% and reduced computational complexity. Additionally, it demonstrates a enhanced F1-Score of 100%, 98%, 98% and 100% for Cyst, Normal, Stone and Tumor respectively.

关键词： Deep learning Accuracy Filters Computational modeling Predictive models Feature extraction Chronic kidney disease data models Diseases Tumors

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：