检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Xiang, Shunian Lawrence, Patrick J. Peng, Bo Chiang, ChienWei Kim, Dokyoon Shen, Li Ning, Xia Biomedical Informatics Department The Ohio State University ColumbusOH43210 United States Computer Science and Engineering Department The Ohio State University ColumbusOH43210 United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA19104 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network’s rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies. © 2023, CC BY.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Reproducibility and Replicability in Neuroimaging: Constrained IVA as an Effective Assessment Tool

Reproducibility and Replicability in Neuroimaging: Constrain...

引用

European Signal Processing Conference (EUSIPCO)

作者： Francisco Laport Adriana Dapena Trung Vu Hanlu Yang Vince Calhoun Tülay Adali Department of Computer Science and Electrical Engineering University of Maryland Baltimore County MD USA CITIC Research Center University of A Coruña A Coruña Spain Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Matrix decomposition techniques have been successfully applied in the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. These data-driven approaches that assume the linear blind source separation (BSS) problem can yield an unsupervised and fully interpretable solution when there is a good model match. However, selecting a suitable model order that provides an accurate model match is an important challenge. Replicability and computational reproducibility are two key aspects that are also intimately related to interpretability. Despite clear evidence that solutions with poor reproducibility can lead to suboptimal results, the evaluation of reproducibility in matrix decomposition techniques remains limited in the existing literature. We propose the use of constrained independent vector analysis (cIVA), a state-of-the-art joint BSS technique, to assess the influence of model order selection for replicability and reproducibility. We demonstrate the attractiveness of clVA for replicability by alleviating permutation ambiguity as well as enabling additional quantification opportunities. Our results show that highly reproducible model orders achieve a good model match with highly interpretable and replicable solutions when clVA is applied to four different restina-state fMRI datasets.

关键词： Analytical models Computational modeling Europe Functional magnetic resonance imaging Signal processing Reproducibility of results Vectors Blind source separation Matrix decomposition Replicability

来源：评论

学校读者我要写书评

暂无评论

Image Fusion in Remote Sensing: An Overview and Meta-Analysis

SSRN

引用

SSRN 2024年

作者： Albanwan, Hessah Qin, Rongjun Tang, Yang Geospatial Data Analytics Laboratory The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering The Ohio State University 218B Bolz Hall 2036 Neil Avenue ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University 205 Dreese Lab 2036 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University United States

Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatio-temporally coherent images. It greatly facilitates downstream applications such as pan-sharpening, change detection, land-cover classification, etc. Yet, image fusion solutions are highly disparate to various remote sensing problems and thus are often narrowly defined in existing reviews as topical applications, such as pan-sharpening and spatial-temporal image fusion. Considering that image fusion can be theoretically applied to any gridded data through pixel-level operations, in this paper, we expand its scope by comprehensively surveying relevant works with a simple taxonomy: 1) many-to-one image fusion;2) many-to-many image fusion. This simple taxonomy defines image fusion as a mapping problem that turns either a single or a set of images into another single or set of images, depending on the desired coherence, e.g., spectral, spatial/resolution coherence, etc. We show that this simple taxonomy, despite the significant modality difference it covers, can be presented by a conceptually easy framework. Furthermore, we provide a meta-analysis to review the major papers studying the various types of image fusion and their applications over the years (from the 1980s to date), covering 10,420 peer-reviewed papers. Finally, we discuss image fusion's main benefits and emerging challenges to provide open research directions and potential future works. © 2024, The Authors. All rights reserved.

关键词： Image fusion

来源：评论

学校读者我要写书评

暂无评论

Gamification and Serious Games for Cybersecurity Awareness and First Responders Training: An overview

TechRxiv

引用

TechRxiv 2023年

作者： Batzos, Zisis Saoulidis, Theocharis Margounakis, Dimitrios Fountoukidis, Eleftherios Grigoriou, Elisavet Moukoulis, Achilleas Sarigiannidis, Antonios Liatifis, Athanasios Karypidis, Paris-Alexandros Bibi, Stamatia Filippidis, Adam Kazanidis, Ioannis Nifakos, Sokratis Kasig, Timo Heydari, Mohammad Mouratidis, Haralambos Sidroco Holdings Ltd. Nicosia Cyprus Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Computer Science International Hellenic University Kavala Campus Greece Massive Dynamic Sweden Sweden Dept. of Computer and Systems Sciences Stockholm University Sweden Institute for Analytics and Data Science University of Essex United Kingdom

Gamification has been characterized as the usage of video game elements in non-game environments. Gamification in cybersecurity awareness training can lead to more engaging interactions, greater pleasure, and increased security. Serious games are becoming increasingly popular since they allow learning in a natural setting. Although many of the principles covered in cybersecurity awareness training are universal, many forms of training fail because they rely on rote learning and do not require users to think about security concepts. The primary goal of this research is to conduct a review of the literature on cybersecurity awareness training methodologies and frameworks, as well as serious games that may be used to raise awareness among people, organizations, and first responders. © 2023, CC BY.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

An Improved Unbalanced data Classification Method Based on Hybrid Sampling Approach 4

An Improved Unbalanced Data Classification Method Based on H...

引用

2021 IEEE 4th International Conference on Big data and Artificial Intelligence, BDAI 2021

作者： Xu, Biru Wang, Wenjia Yang, Rui Han, Qi University of Liverpool Department of Computer Science Liverpool United Kingdom School of Advanced Technology Research Institute of Big Data Analytics Xi an Jiaotong-Liverpool University Suzhou China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781665412704

The problem of data imbalance has received far- reaching concerns since they could affect the accuracy of classification problem in the area of machine learning. As the minority class instances can be ignored by traditional classifiers, it is necessary to improve the recognition rate of minority instances. Therefore, the paper proposes a new hybrid sampling method to solve the data imbalance problem by enlarging the proportion of minority instances. For the oversampling part, a variant of SMOTE is provided combining methods of LR-SMOTE and CCR (Combined Cleaning and Resampling Algorithm);for the under-sampling part, the Tomek-link method is utilized to complete the task. After the pre-processing stage, the data set is classified by Random Forest (RF). Experimental results show that the novel algorithm effectively enhances the performance of RF on the data set with a higher accuracy. © 2021 IEEE.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

A security framework for 5G-enabled wireless sensor networks based on blockchain technology

A security framework for 5G-enabled wireless sensor networks...

引用

Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： M S P Durgarao Dinesh Valluru S. Saraswathi P. Malathi K. Thilagam S. Loganathan Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Information Technology MLRITM Engineering CollegeHyderabad Dept of CSE - AIMLVel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of ECE Velammal Engineering College Chennai Tamil Nadu India Department of ECE Paavai Engineering College Namakkal Tamilnadu India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

A novel blockchain-based security architecture is presented in this study as a solution to the difficulties associated with securing 5 G -enabled WSNs. To manage the uncertain and limited resources inherent in WSNs, the system employs cryptographic techniques, intelligent contracts, and decentralized consensus protocols. The findings of this exhaustive analysis indicate that the proposed approach significantly enhances the overall security stance of 5 G -enabled WSNs. This solution to reduce security risks and vulnerabilities in WSNs is practical and contributes to the growing body of knowledge regarding secure communication in the 5 G era. Subsequent investigations and practical implementations that integrate blockchain technology with 5G-enabled WSNs are made possible by these results.

关键词： Knowledge engineering Wireless sensor networks Scalability data integrity Network security Security Resource management

来源：评论

学校读者我要写书评

暂无评论

Integration of AI and Cloud-Native Technologies for Personalized Mobile Banking Experiences

Integration of AI and Cloud-Native Technologies for Personal...

引用

Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： N. Legapriyadharshini T. Thirumalaikumari K. Senbagam R. Sarasu H. Anwer Basha P. Malathi Department of Computer Application Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

In today's rapidly evolving digital landscape, using Artificial Intelligence (AI) and Cloud-Native Technologies is essential for improving personalized mobile banking experiences. The limitations and errors of existing financial systems necessitate such integration. Existing systems often struggle to adapt to modern consumers dynamic tastes and expectations, resulting in unsatisfactory customer experiences. Identifying the gap, the proposed system revolutionizes mobile banking applications by leveraging AI algorithms such as machine learning and natural language processing within a microservices-based cloud infrastructure. As a result, the proposed system solves the limitations of outdated architectures by providing personalized and efficient services while addressing security issues. The study indicates considerable gains in terms of user pleasure, system scalability, and security robustness through an in-depth evaluation of the results. The results highlight AI's and Cloud-Native Technologies transformational potential in revolutionizing the landscape of tailored mobile banking experiences. The study provides helpful information to financial institutions looking to adopt innovation and fulfill the evolving needs of modern consumers.

关键词： Technological innovation Scalability Microservice architectures Banking Predictive models User experience Robustness Natural language processing Security Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Parallelized bayesian optimization for problems with expensive evaluation functions 20

Parallelized bayesian optimization for problems with expensi...

引用

2020 Genetic and Evolutionary Computation Conference, GECCO 2020

作者： Rebolledo, Margarita Rehbach, Frederik Eiben, A.E. Bartz-Beielstein, Thomas Institute for Data Science Engineering and Analytics TH Köln Gummersbach Germany Department of Computer Science Vrije Universiteit Amsterdam Netherlands

ISBN: (纸本)9781450371278

Many black-box optimization problems rely on simulations to evaluate the quality of candidate solutions. These evaluations can be computationally expensive and very time-consuming. We present and approach to mitigate this problem by taking into consideration two factors: The number of evaluations and the execution time. We aim to keep the number of evaluations low by using Bayesian optimization (BO) - known to be sample efficient - and to reduce wall-clock times by executing parallel evaluations. Four parallelization methods using BO as optimizer are compared against the inherently parallel CMA-ES. Each method is evaluated on all the 24 objective functions of the Black-Box-Optimization-Benchmarking test suite in their 20-dimensional versions. The results show that parallelized BO outperforms the state-of-the-art CMA-ES on most of the test functions, also on higher dimensions. © 2020 Owner/Author.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Approaches for Proactive Phishing Attack Detection

Machine Learning Approaches for Proactive Phishing Attack De...

引用

International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： B. Geetha P. Malathi T. Thirumalaikumari V. Janakiraman H. Anwer Basha S. Rukmani Devi Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Phishing attacks remain a serious problem in cybersecurity, necessitating advances in detection methods. The study describes a novel method for detecting phishing attacks that use advanced machine learning (ML) techniques, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs and RNNs were chosen because of their ability to detect spatial and sequential patterns in phishing data. The system achieves improved performance by iterative model refinement and ensemble learning, beating existing rule-based systems in terms of precision (96%), recall (94%), and F1-score (95%). Furthermore, the proposed strategy has significantly lower false positive (3.8%) and false negative (5.7%) rates than existing systems, indicating greater dependability in distinguishing between legitimate and malicious emails. Cross-validation validates that the proposed system is stable and ready for real-world use. As a result, the proposed ML-driven strategy represents a substantial leap in phishing detection, providing increased accuracy (F1-score: 95%) and resilience to emerging cybersecurity threats.

关键词： Training Recurrent neural networks Accuracy Systematics Phishing Computational modeling Training data

来源：评论

学校读者我要写书评

暂无评论

Automation Through Machine Learning: Reinvigorating Cyber Security

Automation Through Machine Learning: Reinvigorating Cyber Se...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Jayashree V. Bagade Sukhvinder Singh Dari Rekha Dhivrani Santosh Chowhan G. Ravivarman V Janakiraman Department of Information Technology Vishwakarma Institute of Technology Pune india Symbiosis Law School Nagpur Symbiosis International Pune India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Data Analytics and Mathematical Sciences Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cyber security is predicated heavily on manual efforts to fight malicious assaults, but such processes are increasingly turning into overwhelmed by means of the quantity of threats - a hassle worsened in the course of the global COVID-19 pandemic. This has highlighted the importance of leveraging device studying (ML) to automate cyber protection methods. In this article, we speak how ML may be used to optimize the detection, prevention, and remediations of malicious assaults in an extensive kind of applications and offerings. We discover the various procedures being evolved that may assist make the identity of threats greater accurate and greater effortlessly to be had. We additionally recollect the capacity challenges associated with the software of ML strategies, along with the potential for unfair bias in opposition to positive instructions of customers, and become aware of methods for addressing those issues. Our end emphasizes the significance of leveraging ML in order to enable automation in cyber safety, as well as to make sure the detection of state-of-the-art new threats.

关键词： Automation Pandemics Prevention and mitigation Manuals Machine learning Turning Software Security Protection computer crime

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：