检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Bera, Asish Dey, Ratnadeep Bhattacharjee, Debotosh Nasipuri, Mita Shum, Hubert P.H. Department of Computer Science Edge Hill University Lancashire OrmskirkL39 4QP United Kingdom Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Center for Basic and Applied Science Faculty of informatics and management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science Durham University Stockton Road DurhamDH1 3LE United Kingdom

Recent research on biometrics focuses on achieving a high success rate of authentication and addressing the concern of various spoofing attacks. Although hand geometry recognition provides adequate security over unauthorized access, it is susceptible to presentation attack. This paper presents an anti-spoofing method toward hand biometrics. A presentation attack detection approach is addressed by assessing the visual quality of genuine and fake hand images. A threshold-based gradient magnitude similarity quality metric is proposed to discriminate between the real and spoofed hand samples. The visual hand images of 255 subjects from the Bogazici University hand database are considered as original samples. Correspondingly, from each genuine sample, we acquire a forged image using a Canon EOS 700D camera. Such fake hand images with natural degradation are considered for electronic screen display based spoofing attack detection. Furthermore, we create another fake hand dataset with artificial degradation by introducing additional Gaussian blur, salt and pepper, and speckle noises to original images. Ten quality metrics are measured from each sample for classification between original and fake hand image. The classification experiments are performed using the k-nearest neighbors, random forest, and support vector machine classifiers, as well as deep convolutional neural networks. The proposed gradient similarity-based quality metric achieves 1.5% average classification error using the k-nearest neighbors and random forest classifiers. An average classification error of 2.5% is obtained using the baseline evaluation with the MobileNetV2 deep network for discriminating original and different types of fake hand samples. Copyright © 2021, The Authors. All rights reserved.

关键词： k-nearest neighbors

来源：评论

学校读者我要写书评

暂无评论

SmartCHANGE: AI-based long-term health risk evaluation for driving behaviour change strategies in children and youth

SmartCHANGE: AI-based long-term health risk evaluation for d...

引用

International Conference on applied Mathematics & computer Science (ICAMCS)

作者： Nina Reščič Janna Alberts Teatske M. Altenburg Mai J. M. Chinapaw Antonio De Nigro Dario Fenoglio Martin Gjoreski Anton Gradišek Gregor Jurak Athanasios Kiourtis Dimosthenis Kyriazis Marc Langheinrich Elena Mancuso Argyro Mavrogiorgou Mykola Pechenizkiy Roberto Pratola José Ribeiro Maroje Sorić Fawad Taj Tuija H. Tammelin Martijn Vastenburg Anna Vilanova Tanja G. M. Vrijkotte Mitja Luštrek Department of Intelligent Systems Jožef Stefan Institute Ljubljana Slovenia Research & Development ConnectedCare Nijmegen The Netherlands Department of Public and Occupational Health Amsterdam UMC Amsterdam Public Health Research Institute Vrije Universiteit Amsterdam Amsterdam The Netherlands Engineering Ingegneria Informatica S.p.A. Rome Italy Faculty of Informatics Università Della Svizzera Italiana Lugano Switzerland Faculty of Sports University of Ljubljana Ljubljana Slovenia Department of Digital Systems University of Piraeus Piraeus Greece Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Faculty of Sport University of Porto Porto Portugal Likes School of Health and Social Studies Jamk University of Applied Sciences Jyväskylä Finland

The SmartCHANGE project, a Horizon-Europe Research & Innovation project that has been ongoing since May 2023 and is scheduled to conclude in April 2027, aims to develop AI-driven decision-support tools to identify and reduce the long-term risk of non-communicable chronic diseases (NCDs) in children and youth. NCDs have become a significant public health burden in developed countries, with common risk factors including obesity, low physical fitness, and unhealthy lifestyle habits. Childhood and adolescence are critical periods for establishing healthy habits, and early intervention can prevent or delay the onset of NCDs later in life. However, current tools for identifying high-risk individuals mainly focus on adults, leading to missed opportunities for early detection. The SmartCHANGE project aims to address this gap by creating trustworthy AI tools that accurately assess risk factors in children and youth and promote optimised strategies for risk reduction. The paper outlines the plan for the project from the project’s proposed methodologies, including dataset utilisation, data cleaning, risk prediction models (basic and complex), and the implementation of federated learning. Additionally, it discusses user experience aspects such as participatory design, suggested applications, and the evaluation of project outcomes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

BiLSTM with Data Augmentation using Interpolation Methods to Improve Early Detection of Parkinson Disease

BiLSTM with Data Augmentation using Interpolation Methods to...

引用

Federated Conference on computer Science and Information Systems (FedCSIS)

作者： Olusola O. Abayomi-Alli Robertas Damaševičius Rytis Maskeliūnas Adebayo Abayomi-Alli Department of Software Engineering Kaunas University of Technology Kaunas Lithuania Department of Applied Informatics Vytautas Magnus University Kaunas Lithuania Department of Computer Science Federal University of Agriculture Abeokuta Nigeria

ISBN: (数字)9788395541674

ISBN: (纸本)9781728141473

The lack of dopamine in the human brain is the cause of Parkinson disease (PD) which is a degenerative disorder common globally to older citizens. However, late detection of this disease before the first clinical diagnosis has led to increased mortality rate. Research effort towards the early detection of PD has encountered challenges such as: small dataset size, class imbalance, overfitting, high false detection rate, model complexity, etc. This paper aims to improve early detection of PD using machine learning through data augmentation for very small datasets. We propose using Spline interpolation and Piecewise Cubic Hermite Interpolating Polynomial (Pchip) interpolation methods to generate synthetic data instances. We further investigate on reducing dimensionality of features for effective and real-time classification while considering computational complexity of implementation on real-life mobile phones. For classification we use Bidirectional LSTM (BiLSTM) deep learning network and compare the results with traditional machine learning algorithms like Support Vector Machine (SVM), Decision Tree, Logistic regression, KNN and Ensemble bagged tree. For experimental validation we use the Oxford Parkinson disease dataset with 195 data samples, which we have augmented with 571 synthetic data samples. The results for BiLSTM shows that even with a holdout of 90%, the model was still able to effectively recognize PD with an average accuracy for ten rounds experiment using 22 features as 82.86%, 97.1%, and 96.37% for original, augmented (Spline) and augmented (Pchip) datasets, respectively. Our results show that proposed data augmentation schemes have significantly (p

关键词：

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach” [Energy Build. 313 (2024) 114254]

引用

Energy and Buildings 2024年 315卷

作者： Celal Cakiroglu Yaren Aydın Gebrail Bekdaş Umit Isikdag Aidin Nobahar Sadeghifam Laith Abualigah Department of Civil Engineering Turkish-German University Istanbul 34820 Turkey Department of Civil Engineering Istanbul University-Cerrahpaşa Istanbul 34320 Turkey Department of Informatics Mimar Sinan Fine Arts University Istanbul 34427 Turkey Department of Civil Engineering Curtin University Malaysia Miri 98009 Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to "Artificial intelligence-driven predictive framework for early detection of still birth" [SLAS Technology Volume 29, Issue 6, 100203, December 2024]

引用

SLAS technology 2025年 31卷 100236页

作者： Sarah A Alzakari Asma Aldrees Muhammad Umer Lucia Cascone Nisreen Innab Imran Ashraf Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia. Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha Saudi Arabia. Department of Computer Science & Information Technology The Islamia University of Bahawalpur Bahawalpur 63100 Pakistan. Electronic address: umersabir1996@***. Department of Computer Science University of Salerno Fisciano Italy. Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah 13713 Riyadh Saudi Arabia. Information and Communication Engineering Yeungnam University Gyeongsan 38541 Republic of Korea.

来源：评论

学校读者我要写书评

暂无评论

Bounded-degree cut is fixed-parameter tractable

arXiv

引用

arXiv 2020年

作者： Xiao, Mingyu Nagamochi, Hiroshi School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Applied Mathematics and Physics Graduate School of Informatics Kyoto University Japan

In the bounded-degree cut problem, we are given a multigraph G = (V, E), two disjoint vertex subsets A, B ⊆ V , two functions uA, uB : V → {0, 1, . . ., |E|} on V , and an integer k ≥ 0. The task is to determine whether there is a minimal (A, B)-cut (VA, VB) of size at most k such that the degree of each vertex v ∈ VA in the induced subgraph G[VA] is at most uA(v) and the degree of each vertex v ∈ VB in the induced subgraph G[VB] is at most uB(v). In this paper, we show that the bounded-degree cut problem is fixed-parameter tractable by giving a 218k|G|O(1)-time algorithm. This is the first single exponential FPT algorithm for this problem. The core of the algorithm lies two new lemmas based on important cuts, which give some upper bounds on the number of candidates for vertex subsets in one part of a minimal cut satisfying some properties. These lemmas can be used to design fixed-parameter tractable algorithms for more related problems. © 2020, CC BY.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks

arXiv

引用

arXiv 2022年

作者： Grabowski, Bartosz Glomb, Przemyslaw Masarczyk, Wojciech Plawiak, Pawel Yıldırım, Özal Acharya, U. Rajendra Tan, Ru-San Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Warsaw University of Technology Pl. Politechniki 1 Warsaw00-661 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Department of Software Engineering Firat University Elazig Turkey School of Science and Technology Singapore University of Social Sciences Singapore Faculty of Business Education Law and Arts University of Southern Queensland Australia School of Engineering Ngee Ann Polytechnic 535 Clementi Road 599489 Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan Kumamoto University Kumamoto Japan Department of Cardiology National Heart Centre Singapore169609 Singapore Duke-NUS Medical School 169857 Singapore

Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-aided diagnosis. Machine learning tasks can be divided into regression and classification. Regression can be used for noise and artifacts removal as well as resolve issues of missing data from low sampling frequency. Classification task concerns the prediction of output diagnostic classes according to expert-labeled input classes. In this work, we propose a deep neural network model capable of solving regression and classification tasks. Moreover, we combined the two approaches, using unlabeled and labeled data, to train the model. We tested the model on the MIT-BIH Arrhythmia database. Our method showed high effectiveness in detecting cardiac arrhythmia based on modified Lead II ECG records, as well as achieved high quality of ECG signal approximation. For the former, our method attained overall accuracy of 87.33% and balanced accuracy of 80.54%, on par with reference approaches. For the latter, application of self-supervised learning allowed for training without the need for expert labels. The regression model yielded satisfactory performance with fairly accurate prediction of QRS complexes. Transferring knowledge from regression to the classification task, our method attained higher overall accuracy of 87.78%. © 2022, CC BY.

关键词： Electrocardiography

来源：评论

学校读者我要写书评

暂无评论

Natural convection heat transport performance of nanofluids under the influence of inclined magnetic field

引用

Results in Physics 2024年 58卷

作者： Islam, Tarikul Fayz-Al-Asad, Md. Khatun, M.A. Parveen, N. Ahmad, Hijaz Askar, Sameh CMUP University of Porto Porto Portugal Department of Mathematics American International University –Bangladesh Khilkhet Kuratoli Dhaka-1229 Bangladesh Department of Mathematics Jagannath University Dhaka 1000 Bangladesh Department of Mathematics Bangladesh University of Engineering and Technology Dhaka-1000 Bangladesh Near East University Operational Research Center in Healthcare Nicosia PC: 99138 TRNC Mersin 10 Turkey Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Mishref Kuwait Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Mathematics and Informatics Azerbaijan University Jeyhun Hajibeyli street 71 AZ1007 Baku Azerbaijan Department of Statistics and Operations Research College of Science King Saud University P.O. Box 2455 Riyadh 11451 Saudi Arabia

Two-dimensional free convective temperature transportation and fluid flow within the semi-circular cavity occupied by nanofluid with influence of inclined MHD is studied numerically. While bottom wall is warmed by three distinct thermal conditions (uniform, parabolic, and sinusoidal), the upper circular wall is warmed at lower temperatures. A slopping magnetic field acts upon the enclosure whose strength is unchanging. The governing non-linear equations is resolved with the numerical scheme Galerkin type finite element technique. A strong agreement is found with the earlier work. The outcomes for the model parameters likely Rayleigh number, sloping angle of magnetic field, nanoparticles volume, size, shape, and Brownian effects, and Hartmann number are demonstrated through streamlines, isothermal lines, and mean Nusselt number. In addition, investigation is conducted the thermal features of various nanoparticles and the nanofluid produced from them. The outcomes reveal that thermal transfer rate is significantly enhanced by volume fraction nanoparticles and buoyancy-driven parameter Rayleigh number whilst reduces for higher Hartman number. The inclined angles of magnetic field and different thermal conditions have a positive influence on thermal transport. For Cu-H₂O nanofluids, thermal transport is enhanced by 10.98 % in a uniform thermal system, where it increases by 14.42 % in the parabolic thermal system and by 15.53 % in a sinusoidal thermal system for the only 5 % volume of nanoparticles. Moreover, nano-sized particles’ shape factor has a noteworthy role in thermal transport. It is found that heat transfer rate rises by 6.79 % for blade-shaped nanoparticles. Additionally, as considering Brownian effects of nanoparticles, the temperature transport rate is improved by 21.45 % for small-sized nanoparticles. © 2024 The Authors

关键词： Finite Element Method Inclined magnetic field Nanoparticles Natural convection Semi-circular cavity

来源：评论

学校读者我要写书评

暂无评论

SEADer++: social engineering attack detection in online environments using machine learning

引用

Journal of Information and Telecommunication 2020年第3期4卷 346-362页

作者： Lansley, Merton Mouton, Francois Kapetanakis, Stelios Polatidis, Nikolaos Department of Informatics University of Sussex Brighton UK Department of Applied Science Noroff University College Oslo Norway Department of Computer Science University of the Western Cape Bellville South Africa Department of Computer Science University of the Western Cape Bellville South Africa School of Computing Engineering and Mathematics University of Brighton Brighton UK

ABSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to *** engineering attacks are one of the most well-known and easiest to apply attacks in the cybersecurity domain. Research has shown that the majority of attacks against computer systems was based on the use of social engineering methods. Considering the importance of emerging fields such as machine learning and cybersecurity we have developed a method that detects social engineering attacks that is based on natural language processing and artificial neural networks. This method can be applied in offline texts or online environments and flag a conversation as a social engineering attack or not. Initially, the conversation text is parsed and checked for grammatical errors using natural language processing techniques and then an artificial neural network is used to classify possible attacks. The proposed method has been evaluated using a real dataset and a semi-synthetic dataset with very high accuracy results. Furthermore, alternative classification methods have been used for comparisons in both datasets.

关键词： Social engineering online chat environments natural language processing machine learning neural networks ensemble learning

来源：评论

学校读者我要写书评

暂无评论

REAL-TIME GEOINFORMATION SYSTEMS TO IMPROVE THE QUALITY, SCALABILITY, AND COST OF INTERNET OF THINGS FOR AGRI-ENVIRONMENT RESEARCH

arXiv

引用

arXiv 2024年

作者： Runck, Bryan C. Schulz, Bobby Bishop, Jeff Carlson, Nathan Chantigian, Bryan Deters, Gary Erdmann, Jesse Ewing, Patrick M. Felzan, Michael Fu, Xiao Greyling, Jan Hogan, Christopher J. Hollman, Andrew Joglekar, Ali Junker, Kris Kantar, Michael Kaunda, Lumbani Krishna, Mohana Lynch, Benjamin Marchetto, Peter Marsolek, Megan McKay, Troy Morris, Brad Niaghi, Ali Rashid Pamulaparthy, Keerthi Pardey, Philip Piotrowski, Ann Poudyal, Christina Prather, Tom Raghavan, Barath Reiter, Maggie Rosen, Lucas Salazar, Benjamin Scobbie, Andrew Sharma, Vasudha Silverstein, Kevin A.T. Singh, Gurparteet Strock, Jeff Subedi, Samikshya Tang, Evan Turturillo, Gianna Watkins, Eric Webster, Blake Wilgenbusch, James GEMS Informatics Center University of Minnesota Twin Cities United States Department of Geography Environment and Society University of Minnesota Twin Cities United States Minnesota Supercomputing Institute University of Minnesota United States Department of Electrical Engineering University of Minnesota Twin Cities United States Department of Applied Economics University of Minnesota Twin Cities United States Department of Horticultural Science University of Minnesota Twin Cities United States Department of Computer Science University of Southern California United States Sensing Inc. Conservify Graphspan LLC USDA-ARS Food Systems Research Unit BurlingtonVT05405 United States Amazon United States Department of Soil Water and Climate University of Minnesota Twin Cities United States Department of Computer Science and Engineering University of Minnesota Twin Cities United States Departmnet of Agricultural Education Communication and Marketing University of Minnesota Twin Cities United States Benson Hill United States Southwest Research and Outreach Center University of Minnesota Twin Cities United States Code Weavers Department of Mechanical Engineering University of Minnesota Twin Cities United States University of Hawaii at Manoa HonoluluHI United States Minnesota Agricultural Experiment Station University of Minnesota Twin Cities United States Sunday Stellenbosch AgroInformatics Initiative Stellenbosch University South Africa Ameriprise Financial Services LLC Optum University of Minnesota Twin Cities United States

With the increasing emphasis on machine learning and artificial intelligence to drive knowledge discovery in the agricultural sciences, spatial internet of things (IoT) technologies have become increasingly important for collecting real-time, high resolution data for these models. However, managing large fleets of devices while maintaining high data quality remains an ongoing challenge as scientists iterate from prototype to mature end-to-end applications. Here, we provide a set of case studies using the framework of technology readiness levels for an open source spatial IoT system. The spatial IoT systems underwent 3 major and 14 minor system versions, had over 2,727 devices manufactured both in academic and commercial contexts, and are either in active or planned deployment across four continents. Our results show the evolution of a generalizable, open source spatial IoT system designed for agricultural scientists, and provide a model for academic researchers to overcome the challenges that exist in going from one-off prototypes to thousands of internet-connected devices. © 2024, CC BY-NC-ND.

关键词： Internet of things

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：