检索结果-内蒙古大学图书馆

International Symposium on Symbolic and Numeric Algorithms for Scientific computing (SYNASC)

作者： Branislav Radomirovic Luka Jovanovic Catalin Stoean Miodrag Zivkovic Angelina Njegus Nebojsa Bacanin Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Technical Sciences Singidunum University Belgrade Serbia Department of Computer Science University of Craiova Craiova Romania

The Sun has a significant effect on nearly everything in the solar system. While a relatively tame star, it is nevertheless prone to strong emissions of energy in the form of solar flares. Events frequency follows a cycle of approximately 11 years. At the peak of this cycle, events occur more often. Powerful events can even reach the Earth and cause magnetic storms that can disrupt radio communications, satellite trajectories, and navigation. Particular energetic events can even cause significant harm to power and telecommunications infrastructure. A need for a robust system capable of accurately handling classification is evident. This work proposes an approach based on extreme gradient boosting optimized via metaheuristic algorithms and applied to a real-world observation dataset. Outcomes have been compared to several modern optimization algorithms and the outcomes are promising. The results have been statistically validated as significant, and the best models analyzed to determine feature impacts on classifications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Moving towards FAIR mappings and crosswalks

Moving towards FAIR mappings and crosswalks

引用

2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Martínková, Jana Juty, Nick Beltran, Alejandra Gonzalez Goble, Carole Le Franc, Yann e-Science Data Factory S.A.S.U. Montpellier France Czech Technical University in Prague Faculty of Information Technology Prague160 00 Czech Republic The University of Manchester Department of Computer Science Oxford Road ManchesterM13 9PL United Kingdom Scientific Computing Science and Technology Facilities Council Harwell Campus OX11 0QX United Kingdom

Mappings and crosswalks are key elements to ensure semantic interoperability as well as metadata and data integration between different information systems. Designing FAIR compliant systems requires making sure all the elements that constitute the systems are themselves FAIR to support machine-actionability and automation. This paper describes the ongoing European and international effort to build a framework for FAIR mappings and crosswalks. This framework aims to be generic enough to capture the diverse set of use cases and methodologies across domains and communities. It should be composed of a set of technical recommendations to aid compliance with FAIR principles, a set of models for machine-actionable mappings and crosswalks as well as a practical framework with aligned good practices to support the creation of mappings by scientific communities. Developed in the context of FAIR IMPACT, a Horizon Europe project, this work will be pursued within a more international context as a Research Data Alliance Working Group. © 2024 Copyright for this paper by its authors.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis on Denial of Service attack using UNSW-NB15 Dataset 7

Performance Analysis on Denial of Service attack using UNSW-...

引用

7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational science and Information Management, ICSECS-ICOCSIM 2021

作者： Edzereiq Kamarudin, Imran Faizal Ab Razak, Mohd Firdaus, Ahmad Izham Jaya, M. Ti Dun, Yau University Malaysia Pahang Faculty of Computing College of Computing and Applied Science Pahang Pekan26600 Malaysia SysArmy Sdn Bhd Technical Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665414074

With the advancement of network technology, users can now easily gain access to and benefit from networks. However, the number of network violations is increasing. The main issue with this violation is that irresponsible individuals are infiltrating the network. Network intrusion can be interpreted in a variety of ways, including cyber criminals forcibly attempting to disrupt network connections, gaining unauthorized access to valuable data, and then stealing, corrupting, or destroying the data. There are already numerous systems in place to detect network intrusion. However, the systems continue to fall short in detecting and counter-attacking network intrusion attacks. This research aims to enhance the detection of Denial of service (DoS) by identifying significant features and identifying abnormal network activities more accurately. To accomplish this goal, the study proposes an Intrusion Analysis System for detecting Denial of service (DoS) network attacks using machine learning. The accuracy rate of the proposed method using random forest was demonstrated in our experimental results. It was discovered that the accuracy rate with each dataset is greater than 98.8 percent when compared to traditional approaches. Furthermore, when features are selected, the detection time is significantly reduced. © 2021 IEEE.

关键词： Malware

来源：评论

学校读者我要写书评

暂无评论

Recognition of Colon Neoplasm using Contrast-Aware Capsule Network

Recognition of Colon Neoplasm using Contrast-Aware Capsule N...

引用

International Conference on Adaptive science & Technology (ICAST)

作者： Mavis Serwaa Yeboah Patrick Kwabena Mensah Adebayo Felix Adekoya Mighty Abra Ayidzoe Department of Computer Science and Informatics University of Energy and Natural Resources Sunyani Ghana Department of Computer Science Sunyani Technical University Sunyani Ghana Faculty of Computing Engineering and Mathematical Sciences Catholic University of Ghana Sunyani Ghana

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

Colon neoplasms are the second leading cause of mortality worldwide, and despite advances in endoscopy, even experienced endoscopists miss 22-28% of cases. Intelligent systems can bridge this gap, but current approaches face significant challenges. For instance, Convolutional Neural Networks (CNNs) excel in image recognition but require large datasets, which are often scarce in healthcare. On the other hand, Capsule Networks, suitable for smaller datasets, struggle with complex images. To address these limitations, we propose a novel contrast-aware Capsule Network for colon neoplasm recognition. By incorporating a novel contrast-aware feature transformation algorithm, our proposed model enhances extracted features and focuses on processing relevant features, achieving remarkable recognition accuracies of 96.70%, 93.68%, and 99.99% on PolyGen, ETIS-Larib, and CVC-ClinicDB datasets, respectively. Our approach outperforms state-of-the-art Capsule Network algorithms and offers a promising solution to support endoscopists in accurate colon neoplasm detection, reducing the burden of image analysis and improving patient outcomes.

关键词： Uncertainty Accuracy Neural networks Estimation Training data Mortality Medical services Feature extraction Colon Neoplasms

来源：评论

学校读者我要写书评

暂无评论

Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

引用

Computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

来源：评论

学校读者我要写书评

暂无评论

IoT Based Alcohol Detection and Vehicle control System 5

IoT Based Alcohol Detection and Vehicle Control System

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Sapthami, I. Raju, V. V. Narasimha Vaithianathan, V. Chinnasamy, P. Kumaran, G. Ayyasamy, Ramesh Kumar Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad500043 India Sri Sivasubramaniya Nadar College of Engineering Department of Electronics and Communication Engineering Kalavakkam Chennai603110 India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering TamilNadu Srivilliputtur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Faculty of Information and Communication Technology Kampar Malaysia

ISBN: (纸本)9798350389609

It is of the utmost importance to be aware of safety measures when operating a motor vehicle in the contemporary world, characterized by a fast-paced and hazardous environment. It has become necessary to develop innovative technology in order to improve road safety as a result of the alarmingly high number of incidents that can be linked to drivers who are under the influence of alcohol. As a solution to this significant problem, this study uses the capabilities offered by the Internet of Things (IoT) to develop an integrated system for detecting and controlling alcohol in commercial vehicles. This study aims to reduce the dangers connected with driving under the influence of alcohol by allowing for the seamless integration of cutting-edge alcohol detection technologies with real-time vehicle control mechanisms. The system makes use of specific sensors for the detection of alcohol. It interacts with the control mechanisms of the vehicle in order to restrict its functioning when alcohol is present. To make this possible, the hardware component of the system mounted in the vehicle uses the MQ3 sensor, also known as the alcohol sensor. This sensor is responsible for monitoring the alcohol molecules in the air around the driver to determine whether or not the driver is intoxicated. A buzzer, a DC motor, an LCD, a GPS, and a GSM are all components utilized by the system for data transfer. Additionally, the gear is powered by a 12-volt battery. As soon as the system determines that there is alcohol present in the vehicle, it immediately locks the accelerator. Using the GPS, the driver can obtain their location coordinates. © 2024 IEEE.

关键词： Highway accidents

来源：评论

学校读者我要写书评

暂无评论

Permutation Superposition Oracles for Quantum Query Lower Bounds

arXiv

引用

arXiv 2024年

作者： Majenz, Christian Malavolta, Giulio Walter, Michael Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Computing Sciences Bocconi University Italy Max Planck Institute for Security and Privacy Germany Faculty of Computer Science Ruhr-Universität Bochum Germany

We propose a generalization of Zhandry's compressed oracle method to random permutations, where an algorithm can query both the permutation and its inverse. We show how to use the resulting oracle simulation to bound the success probability of an algorithm for any predicate on input-output pairs, a key feature of Zhandry's technique that had hitherto resisted attempts at generalization to random permutations. One key technical ingredient is to use strictly monotone factorizations to represent the permutation in the oracle's database. As an application of our framework, we show that the one-round sponge construction is unconditionally preimage resistant in the random permutation model. This proves a conjecture by Unruh. © 2024, CC BY.

关键词： Query processing

来源：评论

学校读者我要写书评

暂无评论

Application of Image Processing and Machine Learning Technologies for Home Care Bed

Application of Image Processing and Machine Learning Technol...

引用

2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Suponenkovs, Artjoms Lacis, Matiss Markovics, Zigurds Kovalovs, Mihails Platkajis, Ardis Kalnins, Andrejs Process Engineering and Equipment Manufacturing Company PERUZA Riga Latvia Riga Technical University Department of Computer Control and Computer Networks Riga Latvia Riga Technical University Faculty of Computed Science and Information Technology Riga Latvia Riga Stradins University Department of Radiology Riga Latvia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

The problem of analysing a bed bound patient (that is, a patient who is confined to their bed) is a relevant factor in whether or not they will receive timely medical care. This paper aims to investigate the challenges and possible solutions to such problems. It describes an approach and methods for such analysis, as well as for home care bed control. This paper also addresses the challenges of Image Processing and Machine Learning. The experimental part describes important aspects regarding analysing brain activity, facial expression, blood flow, and human body pose. These experimental results and the proposed approach based upon them can be helpful for ensuring timely medical care. In addition, the results of this work can be useful for home care bed development. © 2022 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Simplifying Graph-Parallel Computation in Apache Spark with GraphX 8

Simplifying Graph-Parallel Computation in Apache Spark with ...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Vyakaranam, Lasya Raman, Ramakrishnan Shah, Harshal Mishra, Nilamadhab Meenakshi, R. Murugan, S. Pune Maharashtra Pune India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Streamlining Apache graph-parallel computing the goal of integrating Spark with GraphX is to make processing massive amounts of graph data easier and faster. The goal is to streamline the process of creating and running graph algorithms by using GraphX, a Spark-based distributed graph processing platform. The eventual objective is to create a scalable platform that is easy to use for graph-structured data analysis and manipulation. Improving GraphX's performance, expanding its features, and integrating it with Spark's ecosystem are all part of this process. The expected result is a flexible and strong framework that lets data scientists and developers execute complicated graph analytics tasks with ease. This will pave the way for applications in many fields, including recommendation systems, social network analysis, and biological network analysis. Optimizing performance in graph-parallel computer situations, making the development process easier, and making it more scalable are the main goals. The graph structure visualization results in graph data revealing values in the range of 1.2 to 5.6 for a sample of five distinct vertex/edge types. The degree distribution histogram in the same dataset shows that, for five different frequencies, the numbers range from around 1.5 to 4.5. For the same dataset, the PageRank Convergence Plot shows values ranging from 0.4 to 0.9 for five distinct iterations/scores. © 2024 IEEE.

关键词： Apache Spark Big Data Graph Analytics Graph-Parallel Computation GraphX

来源：评论

学校读者我要写书评

暂无评论

Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction

引用

Neural computing and Applications 2024年第24期36卷 14727-14756页

作者： Jovanovic, Luka Zivkovic, Miodrag Bacanin, Nebojsa Dobrojevic, Milos Simic, Vladimir Sadasivuni, Kishor Kumar Tirkolaee, Erfan Babaee Faculty of Technical Sciences Singidunum University Danijelova 32 Belgrade11000 Serbia Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Mathematics Saveetha School of Engineering SIMATS Tamilnadu Thandalam Chennai602105 India MEU Research Unit Middle East University Amman Jordan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade11010 Serbia Department of Industrial Engineering and Management College of Engineering Yuan Ze University Yuandong Rd. Zhongli Dist. Taoyuan City320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Center for Advanced Materials Qatar University Doha Qatar Department of Industrial Engineering Istinye University Istanbul34396 Turkey Department of Industrial and Mechanical Engineering Lebanese American University Byblos Lebanon

This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared (R2) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. © The Author(s) 2024.

关键词： Crops

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：