检索结果-内蒙古大学图书馆

IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Beth Plale Preeti Malakar Meenakshi D'Souza Hemangee K. Kapoor Yogesh Simmhan Ilkay Altintas Manohar Swaminathan School of Informatics Computing and Engineering Indiana University Bloomington IN USA Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kalyanpur Kanpur India International Institute of Information Technology Bangalore Bengaluru Karnataka India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati Assam India Department of Computational and Data Sciences Indian Institute of Science Bangalore India San Diego Supercomputer Center University of California San Diego MC USA Microsoft Research Lab - India Bengaluru Karnataka India

“CCGRID will act with responsibility as its primary consideration; with equity, diversity, and inclusion as its central goals.” from the CCGRID 2023 web site [1]

关键词：

来源：评论

学校读者我要写书评

暂无评论

PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction

PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutra...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhang Wan Zhuoyi Lin Shamima Rashid Shaun Yue-Hao Ng Rui Yin J. Senthilnath Chee-Keong Kwoh School of Computer Science and Engineering Nanyang Technological University Singapore Institute for Infocomm Research Agency for Science Technology and Research (A*STAR) Singapore School of Chemical and Biomolecular Engineering Nanyang Technological University Singapore Department of Health Outcome and Biomedical Informatics University of Florida Gainesville FL USA

Prediction of neutralization antibodies is important for the development of effective vaccines and antibody-based therapeutics. Traditional methods rely on features based on first principles derived from the binding interface. However, they are burdened by arduous data preprocessing from a limited quantity of protein structures. In comparison, deep learning allows automatic substructure characterization and representation without hand-crafted feature engineering. In particular, large language models (LLMs) based method predicts neutralization using Fv sequences of antibody and antigen. Despite LLM’s success, incorporating full-length Fv sequences suffers from: 1) inaccurate sequence-level labels in existing datasets, 2) inefficient modeling due to noisy non-contributing motifs, and 3) ignorance of non-bonded interactions that play a key role in facilitating epitope-paratope pairing. In this paper, we propose a novel approach that incorporates only the paratope and epitope for antibody-antigen neutralization prediction while adopting a novel set modeling that regards the paratope and epitope as bags of residues. Specifically, we hand-crafted a dataset containing neutralizing paratope-epitope pairs where epitopes are potentially generalizable to future unseen variants of SARS-CoV-2. Training on such a dataset enables deep learning models to predict neutralizing antibodies for prospective mutated variants of SARS-CoV-2, meanwhile addressing the problem of inaccurate sequence-level labels. A higher modeling efficiency is also achieved by disregarding non-contributing motifs. Furthermore, we also propose paratope-epitope set interaction (PESI), a set modeling model inspired by first principles that learns intra-inter non-covalent interactions through a global attention mechanism. To validate PESI, we perform a 10-fold cross-validation on our dataset. Experimental results show that PESI achieves a more balanced overall performance and a significant improvement on MCC as c

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction of water quality parameters by IoT and machine learning

Prediction of water quality parameters by IoT and machine le...

引用

International Conference on computer Communication and informatics (ICCCI)

作者： Srinivas Kolli M. Ranjani P. Kavitha D. Arul Pon Daniel Awadhesh Chandramauli Department Of Information Technology VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad India Computer Applications Jawaharlal Nehru college for women Ulundurpet India Muthayammal center for Advanced Research Muthayammal College of Arts & Science Department of Computer Science and Applications Loyola College of Arts & Science Department of Civil Engineering Uttaranchal Institute of Technology Uttaranchal University Dehrradun India

Increases in domestical applications and other applications drive the need for better clean Water Management in India. What makes up the “chemical, physical, and biological” quality of freshwater There are three stages in the conventional method of monitoring water quality: sample, analysis, and inquiry. The scientists do these by hand. This method needs to be foolproof and predict a water supply’s quality. They’re Using the development of wireless sensor technologies, has also been some investigation into the possibility of monitoring water quality with wireless sensors put in water and communicating with farmers through brief messages regarding water. There has also been a study into the possibility of employing machine learning algorithms to analyze water quality. In light of recent advances in Machine Communication, which allow for devices to communicate with one another and smart ly analyze the data they collect. We have developed an “smart water quality monitoring system by using wireless sensonrs” for residential areas that rely on water storage tanks. A pH sensor and a total dissolved solids meter are used in this system to provide accurate readings of the water’s pH and dissolved solids levels. K-Means clustering, a machine learning technique, has been used to forecast water quality using a training data set consisting of various water samples. This system is now in its prototype stage, used with low-cost embedded devices like the Arduino Uno and Raspberry Pi3.

关键词： Wireless communication Meters Wireless sensor networks Water storage Training data Prototypes Water quality

来源：评论

学校读者我要写书评

暂无评论

Existence and stability for a nonlinear hybrid differential equation of fractional order via regular Mittag–Leffler kernel

Existence and stability for a nonlinear hybrid differential ...

引用

作者： Slimane, Ibrahim Dahmani, Zoubir Nieto, Juan J. Abdeljawad, Thabet Faculty of Exact Sciences and Informatics UMAB Abdelhamid Ibn Badis University of Mostaganem Mostaganem Algeria Department of Statistics Mathematical Analysis and Optimization Institute of Mathematics University of Santiago de Compostela Santiago de Compostela Spain Department of Mathematics and General Sciences Prince Sultan University Riyadh Saudi Arabia Department of Medical Research China Medical University Taichung Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

This paper deals with a nonlinear hybrid differential equation written using a fractional derivative with a Mittag–Leffler kernel. Firstly, we establish the existence of solutions to the studied problem by using the Banach contraction theorem. Then, by means of the Dhage fixed-point principle, we discuss the existence of mild solutions. Finally, we study the Ulam–Hyers stability of the introduced fractional hybrid problem. © 2021 John Wiley & Sons, Ltd.

关键词： Nonlinear equations

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

引用

Physical Review Letters 2024年第24期132卷 240805-240805页

作者： Sho Sugiura Arkopal Dutt William J. Munro Sina Zeytinoğlu Isaac L. Chuang Physics and Informatics Laboratory NTT Research Inc. 940 Stewart Drive Sunnyvale California 94085 USA Laboratory for Nuclear Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Co-Design Center for Quantum Advantage Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA NTT Basic Research Laboratories and Research Center for Theoretical Quantum Physics 3-1 Morinosato-Wakamiya Atsugi Kanagawa 243-0198 Japan Okinawa Institute of Science and Technology Graduate University Onna-son Okinawa 904-0495 Japan Department of Physics Harvard University Cambridge Massachusetts 02138 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

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. We find sequential protocols achieve perfect discrimination and saturate the Heisenberg limit. In contrast, depth-1 parallel and multishot protocols cannot solve hidden system QCD. This suggests sequential protocols are superior in experimentally realistic situations.

关键词： Quantum measurements Quantum metrology Quantum sensing

来源：评论

学校读者我要写书评

暂无评论

Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking

arXiv

引用

arXiv 2025年

作者： Liu, Zhang Niyato, Dusit Wang, Jiacheng Sun, Geng Huang, Lianfen Gao, Zhibin Wang, Xianbin Department of Informatics and Communication Engineering Xiamen University Fujian361102 China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun130012 China Navigation Institute Jimei University Fujian Xiamen361021 China Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada

Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term decisions while ensuring system stability. This theory is particularly valuable in unmanned aerial vehicle (UAV)-based low-altitude economy (LAE) networking scenarios, where it could effectively address inherent challenges of dynamic network conditions, multiple optimization objectives, and stability requirements. Recently, generative artificial intelligence (GenAI) has garnered significant attention for its unprecedented capability to generate diverse digital content. Extending beyond content generation, in this paper, we propose a framework integrating generative diffusion models with reinforcement learning to address Lyapunov optimization problems in UAV-based LAE networking. We begin by introducing the fundamentals of Lyapunov optimization theory and analyzing the limitations of both conventional methods and traditional AI-enabled approaches. We then examine various GenAI models and comprehensively analyze their potential contributions to Lyapunov optimization. Subsequently, we develop a Lyapunov-guided generative diffusion model-based reinforcement learning framework and validate its effectiveness through a UAV-based LAE networking case study. Finally, we outline several directions for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data Science Training Platform to Increase researcher Diversity and Advance Health Equity 11

AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data...

引用

11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Ahmed, Toufeeq Hoyal, Aidan Stinson, Katie Johnson, Jay Latif, Zainab Burge, Legand L. Libin, Alexander Gordon Gao, Guodong Shara, Nawar Vishwanatha, Jamboor K. Vanderbilt University Medical Center Department of Biomedical Informatics Nashville United States Howard University Department of Electrical Engineering and Computer Science WashingtonDC United States Georgetown University MedStar Health Research Institute WashingtonDC United States Johns Hopkins University Carey Business School Baltimore United States University of North Texas Health Science Center at Fort Worth Institute for Health Disparities Fort Worth United States

ISBN: (纸本)9798350302639

AIM-AHEAD is an NIH-funded consortium whose goal is to advance health equity and researcher diversity via artificial intelligence and machine learning. In our first year of operations, we launched AIM-AHEAD Connect, a centralized platform for the consortium stakeholders containing mentoring, networking, and professional development resources. This paper describes the resources available in AIM-AHEAD Connect as well as collaborative efforts to provide tools and training for the entire consortium. © 2023 IEEE.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Enhanced algorithmic modelling and architecture in deep reinforcement learning based on wireless communication Fintech technology

引用

Optik 2023年 272卷

作者： Upreti, Kamal Syed, Mohammad Haider Khan, Mohiuddin Ali Fatima, Huda Alam, Mohammad Shabbir Sharma, A.K. Department of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology & Management Delhi India College of Computing and Informatics Saudi Electronic University Saudi Arabia Department of Computer & Network Engineering Jazan University Jazan Saudi Arabia Department of Information Technology & Security Jazan University Jazan Saudi Arabia Department of Computer science College of Computer Science and Information Technology Jazan University Jizan Saudi Arabia Department of Creative Technologies & Product Design National Taipei University of Business Taipei City Taiwan

Financial technology" or "FinTech" refers to use of information technologies to derive financial solutions. FinTech is now widely regarded as a hotly debated blend of financial services and information technology. A combination of AI and IoT approaches will significantly increase the extraction of valuable financial data as well as provide better services to customers. This research proposed a novel protocol in data transmission using fuzzy rule-based secure transmission with data optimization technique using deep reinforcement neural network by wireless communication. This technique for data transmission will reduce the credit risk and enhances the data optimization by fuzzy rule-based protocol with DRNN. The logic utilized is fuzzy logic, which is a multi-valued logic with truth values for variables ranging from 0 to 1. When truth values range from entirely true to completely false, it is used. The end user's searching experience is improved by fuzzy logic-based semantic search, which finds and retrieves exact matching files for relevant search files provided by user. When exact matches aren't possible, method leverages semantic similarities to determine most relevant matches. A grading method is utilized to minimize number of false positives. For this, proposed technique was used, which may decide on a collection of suitable storage servers on which the data must be saved and resulting in a reduction in execution time while maintaining a higher level of security. The experimental results show the execution time of 54 %, network performance of 96 %, the overall complexity of 71 %, data optimization of 95 % and end-end delay of 67 %. © 2022

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Cloud-based Distributed Computing Approach for Extracting Molecular Descriptors 22

A Cloud-based Distributed Computing Approach for Extracting ...

引用

Proceedings of the 6th International Conference on Algorithms, Computing and Systems

作者： Christos Didachos Dionisis Panagiotis Kintos Manolis Fousteris Vassilis C. Gerogiannis Le Hoang Son Andreas Kanavos Department of Computer Engineering & Informatics University of Patras Greece Department of Pharmacy Laboratory of Medicinal Chemistry University of Patras Greece Department of Digital Systems University of Thessaly Greece VNU Information Technology Institute Vietnam National University Vietnam Department of Informatics Ionian University Greece

ISBN: (纸本)9781450397407

Feature extraction is an initial and essential part for the development of accurate predictive machine learning classifiers. In the research field of drug discovery and development, the usage of molecular descriptors, which can be defined as mathematical representations of molecules’ chemical properties, is a challenging task not only for machine learning studies but even for "classical wet lab" approaches. However, a high diversity of these descriptors is required in order to exploit all the available knowledge and, consequently, to maximize the potentially predictive power of approaches that could be applied for the discovery of new bioactive compounds against one or more molecular targets. Furthermore, the representation and normalization of these information is considered a rather time-consuming process. Herein, we present an approach that employs the power of cloud and distributing computing for the extraction, processing and representation of big datasets, leading to the generation of molecular descriptors in a reasonable time frame.

关键词： Coiled Computing Performance Computational Drug Design Dask Molecular Descriptors Chemical Big Data

来源：评论

学校读者我要写书评

暂无评论

Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep

Advancing Fetal Surveillance with Physiological Sensing: Det...

引用

IEEE SENSORS

作者： Weitao Tang Nhi Tran Nasim Katebi Reza Sameni Gari D. Clifford David Walker Vaishnavi Horlali Callum Taylor Robert Galinsky Faezeh Marzbanrad Electrical and Computer System Engineering Monash University Melbourne Australia The Ritchie Centre Hudson Institute of Medical Research Melbourne Australia Dept. of Biomedical Informatics Emory University Atlanta USA Dept. of Biomedical Engineering Georgia Institute of Technology Emory University Atlanta USA Department of Obstetrics and Gynaecology Monash University Melbourne Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

Accurate detection of hypoxia during fetal monitoring is critical for timely intervention and prevention of brain injury. This study investigated hypoxia detection in fetal sheep by developing a classifier to differentiate between baseline, during acute hypoxia (umbilical cord occlusion; UCO), and post-hypoxia recovery (post-UCO), using physiological signals and an XGBoost machine learning model. A multi-modal approach integrating electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals was compared to a model based on the ECG signal only. We included recordings from 14 fetal sheep, each of which underwent acute hypoxia via a single UCO procedure. Performance of hypoxia detection was measured by Marco F1 score (F1 m ), Accuracy (Acc), and standard deviation of validation error (SDE). Our multi-modal model achieved F1 m = 0.88, Acc = 0.90, while the ECG-based approach achieved F1 m = 0.82, Acc = 0.85. These results indicate that the multi-modal approach was superior to the ECG-only model for overall classification. While ECG and multimodal models correctly classified over 92% and 97% of acute hypoxia intervals, respectively, the classification of post- UCO intervals proved more challenging. The complexity of interpreting physiological signals underscores the need to consider temporal dynamics and physiological context in fetal monitoring.

关键词： Accuracy Machine learning Electrocardiography Hypoxia Brain modeling Feature extraction Electromyography Electroencephalography Complexity theory Biomedical monitoring

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：