检索结果-内蒙古大学图书馆

16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Dimitriou, Theodoros Skondras, Emmanouil Hitiris, Christos Gkola, Cleopatra Papapanagiotou, Ioannis S. Vergados, Dimitrios J. Papapanagiotou, Stavros I. Koumantakis, Stratos Michalas, Angelos Vergados, Dimitrios D. Department of Informatics University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics University of Western Macedonia Kastoria Greece Internet Business Hellas Athens Greece MAS S.A. Athens Greece

ISBN: (纸本)9783031580529

Supervised Machine Learning (ML) algorithms are used for making predictions or decisions based on labeled data. In this paper, an overview about existing supervised ML algorithms is performed. In particular, the algorithms that are studied comprehend the Linear Regression (LR), the Ridge Regression (RR), the Decision Tree (DT), as well as Ensemble algorithms. Subsequently, a comparative analysis of the algorithms is performed using a dataset containing data about ship engines. Effective management of ship engines is important for their robust operation, which can then bring significant economic and environmental benefits. Inferences about the condition of engines and predictions about their performance could prove crucial for specifying optimal cruise parameters, early fault detection and timely service planning. The analysis demonstrates the strength and the weaknesses of each algorithm in terms of predicting decay factors of the ship engine by taking into consideration the data included to the aforementioned dataset. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Security and privacy issues of physical objects in the IoT:Challenges and opportunities

引用

Digital Communications and Networks 2021年第3期7卷 373-384页

作者： Xuanxia Yao Fadi Farha Rongyang Li Ismini Psychoul Liming Chen Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China School of Computer Science and Informatics De Montfort UniversityLeicesterUK

In the Internet of Things(IoT),security and privacy issues of physical objects are crucial to the related *** order to clarify the complicated security and privacy issues,the life cycle of a physical object is divided into three stages of pre-working,in-working,and *** this basis,a physical object-based security architecture for the IoT is put *** to the security architecture,security and privacy requirements and related protecting technologies for physical objects in different working stages are analyzed in *** the development of IoT technologies,potential security and privacy challenges that IoT objects may face in the pervasive computing environment are *** the same time,possible directions for dealing with these challenges are also pointed out.

关键词： Security Privacy preserving Physical objects Life cycle Internet of things

来源：评论

学校读者我要写书评

暂无评论

Selection of a Simple 5G Handover Algorithm: Preparation for Named Data Networking 10

Selection of a Simple 5G Handover Algorithm: Preparation for...

引用

10th International Conference on Wireless and Telematics, ICWT 2024

作者： Nurhayati, Ade Ahdan, Syaiful Juhana, Tutun Syambas, Nana Rachmana Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Telkom University Faculty of Applied Science Jakarta11710 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science 35132 Indonesia

ISBN: (纸本)9798350377057

Named Data Networking as an alternative network for 5G network traffic is required to be able to provide better performance compared to other networks such as internet protocol networks. In NDN wireless, it is known that there is producer mobility and consumer mobility, so optimal mobility management is needed, including the handover process. In this paper the author proposes a simple RSSI-based 5G handover algorithm with distance as a reference RSSI value. This algorithm is a preparation for the handover algorithm on the NDN wireless network. By using the assumed distance value MS to eNB as the basic value for calculating RSSI, handover determination is determined by comparing the RSSI value to the RSSI threshold value. With the scenario of different numbers of users and cluster sizes, from the handover process involving 8 eNB. It can be calculated how big the handover probability is for each user. From the simulation results, it can be concluded that the more users and the larger the cluster size, the greater the handover probability value. This is linear, with the wider the cluster, the probability of getting an RSSI signal from the original eNB becomes smaller, making it possible to get an RSSI signal from neighboring eNB, so that a handover occurs. © 2024 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Real Time Prediction Model for Air Pollution and Air Quality Index based on Machine Learning 1

Real Time Prediction Model for Air Pollution and Air Quality...

引用

1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Kumar, Rakesh Sharma, Bhisham Shekhar, Shashi Dhaou, Imed Ben Singhal, Saurabh GLA University Department of Computer Engineering & Applications Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals' wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Predictive Analysis of Toddler Nutrition Using C5.0 Decision Tree Method

Predictive Analysis of Toddler Nutrition Using C5.0 Decision...

引用

2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Styawati Nurkholis, Andi Alim, Syahirul Samsugi, S. Asyad, Chafidz Universitas Teknokrat Indonesia Information Systems Bandar Lampung Indonesia Informatics UPN 'Veteran' Yogyakarta Yogyakarta Indonesia Universitas Teknokrat Indonesia Computer Engineering Bandar Lampung Indonesia

ISBN: (纸本)9798350376210

The nutritional status of toddlers is one of the benchmarks that can reflect their health. Malnutrition status in toddlers does not occur suddenly but begins with limited weight gain that is not enough. Changes in the toddler's weight from time to time are an early indication of changes in the nutritional status of toddlers. This study aims to determine the nutritional status of a toddler by applying the C5.0 Decision Tree Method. The dataset is divided into two categories: explanatory factors and target class. Explanatory factors are the criteria for determining nutritional status for toddlers, which include gender, age, weight and height, head circumference, and arm circumference. While the target class represents the nutritional status of toddlers, consisting of two classes, namely good and bad. Two prediction models are generated based on the 70:30 and 80:20 data partitions. The 70:30 partition model variation produces an accuracy of 84%, recall is 0.42, precision is 0.56, and f1-score is 0.48. While the 80:20 partition model partition obtained an accuracy of 89%, recall is 0.71, precision is 0.62, and f1-score is 0.67. The best prediction model is expected to be a solution for the community and related stakeholders as a follow-up to prevent malnutrition in toddlers. Comparisons to different algorithms may be made to create a more effective model. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

引用

computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

来源：评论

学校读者我要写书评

暂无评论

Enhancing Emotion Classification with a Hybrid BERT and CNN Architecture

Enhancing Emotion Classification with a Hybrid BERT and CNN ...

引用

International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Orestis Papadimitriou Athanasios Kanavos Gerasimos Vonitsanos Manolis Maragoudakis Panagiotis Karkazis Phivos Mylonas Department of Information and Communication Systems Engineering University of the Aegean Greece Computer Engineering and Informatics Department University of Patras Greece Department of Informatics Ionian University Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (数字)9798331504502

ISBN: (纸本)9798331504519

In computational linguistics, effectively encoding and systematizing emotional expressions in language is a significant challenge. Existing machine learning (ML) models for text analysis often only recognize primary emotional states such as anger, happiness, and sadness, missing the more nuanced spectrum of human emotions. These models frequently overlook the complex semantic interplays and fail to capture the full diversity of emotional expressions, focusing instead on simplistic categorization. This paper proposes a robust ML framework that enhances emotion classification by discriminating among nuanced emotion categories—Anger, Joy, and Fear. Leveraging a sophisticated combination of Convolutional Neural Networks (CNNs) and the Bidirectional Encoder Representations from Transformers (BERT) architecture, our framework demonstrates exceptional accuracy in emotion detection. Evaluating a diverse dataset of text samples, each meticulously tagged with its expressed emotion, confirms the model’s superior performance in recognizing and classifying a broad range of emotional states.

关键词： Emotion recognition Text analysis Text recognition Computational modeling Semantics computer architecture Bidirectional control Transformers Encoding Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

FHRP Performance Measurement Using Etherchannel and Routing protocol OSPFv2, IGRP and BGPv4 on Three-Tier Enterprise Network Architecture Topology

FHRP Performance Measurement Using Etherchannel and Routing ...

引用

International Conference on Cyber and IT Service Management (CITSM)

作者： Arini Andrew Fiade Iik Muhamad Malik Matin Ahmad Solahudin Rifandi Informatics Engineering UIN Syarif Hidayatullah Jakarta Jakarta Indonesia Computer and Informatics Engineering Jakarta State Polytechnic Jakarta Indonesia

ISBN: (数字)9798331528799

ISBN: (纸本)9798331528805

Networks in enterprise scale require connectivity that runs continuously so that a backup network is needed that functions as a substitute for the main network in case of problems. Network problems can affect data traffic and Quality. This can be overcome by using a gateway redundancy protocol. To determine the performance of a computer network when switching from the main network to the backup network, performance measurements are needed. Performance measurements are carried out with several scenarios using TCP and UDP packet transmissions to determine the value of Quality of Service, namely Throughput, Jitter, Packet loss and Delay. The results of this study show a comparison of protocol redundancy (FHRP) using EIGRP and OSPF routing by producing the HSRP protocol has the best QoS performance using EIGRP and OSPF routing but has a delay value that is slightly not better than other combinations in several scenarios

关键词： Redundancy Packet loss Quality of service Logic gates Jitter Network architecture Throughput Routing Routing protocols Delays

来源：评论

学校读者我要写书评

暂无评论

Study of Dimensionality Reduction and Clustering Machine Learning Algorithms for the Analysis of Ship Engine Data 16th

Study of Dimensionality Reduction and Clustering Machine Lea...

引用

16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Dimitriou, Theodoros Skondras, Emmanouil Hitiris, Christos Gkola, Cleopatra Papapanagiotou, Ioannis S. Vergados, Dimitrios J. Fasoula, Georgia Koumantakis, Stratos Michalas, Angelos Vergados, Dimitrios D. Department of Informatics University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics University of Western Macedonia Kastoria Greece Internet Business Hellas Athens Greece MAS S.A. Athens Greece

ISBN: (纸本)9783031580529

Machine Learning (ML) is being successfully applied to ship engine management with proven economic and environmental benefits by engine performance optimization, timely fault detection and appropriate service planning. However, the data preparation for usage in ML algorithms provides several advantages including faster training and improved performance of the algorithm, improved visualization of the dataset, noise reduction, dataset simplification, avoidance of the curse of dimensionality and improved resource utilization. In this paper, two key techniques of the ML algorithms, that can be applied for data preparation and organization of ship engine data are studied, namely the dimensionality reduction and the data clustering. Dimensionality reduction involves the reduce of the number of input variables or features in a dataset, by retaining as much valuable information as possible. On the other hand, clustering ML techniques help to uncover insights and reduce data complexity through the organization of the data into clusters. Evaluation results demonstrate the usefulness of both techniques. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： Engines

来源：评论

学校读者我要写书评

暂无评论

Classification of Adversarial Attacks Using Ensemble Clustering Approach

引用

computers, Materials & Continua 2023年第2期74卷 2479-2498页

作者： Pongsakorn Tatongjai Tossapon Boongoen Natthakan Iam-On Nitin Naik Longzhi Yang Center of Excellence in AI&Emerging Technologies School of ITMae Fah Luang UniversityChiang RaiThailand Department of Computer Science Aberystwyth UniversityAberystwythUnited Kingdom School of Informatics and Digital Engineering Aston UniversityBirminghamUnited Kingdom Department of Computer and Information Sciences Northumbria UniversityNewcastleUnited Kingdom

As more business transactions and information services have been implemented via communication networks,both personal and organization assets encounter a higher risk of *** safeguard these,a perimeter defence likeNIDS(network-based intrusion detection system)can be effective for known *** has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks,where obfuscation techniques are applied to disguise patterns of intrusive *** current research focuses on non-payload connections at the TCP(transmission control protocol)stack level that is applicable to different network *** contrary to the wrapper method introduced with the benchmark dataset,three new filter models are proposed to transform the feature space without knowledge of class *** ECT(ensemble clustering based transformation)techniques,i.e.,ECT-Subspace,ECT-Noise and ECT-Combined,are developed using the concept of ensemble clustering and three different ensemble generation strategies,i.e.,random feature subspace,feature noise injection and their *** on the empirical study with published dataset and four classification algorithms,new models usually outperform that original wrapper and other filter alternatives found in the *** is similarly summarized from the first experiment with basic classification of legitimate and direct attacks,and the second that focuses on recognizing obfuscated *** addition,analysis of algorithmic parameters,i.e.,ensemble size and level of noise,is provided as a guideline for a practical use.

关键词： Intrusion detection adversarial attack machine learning feature transformation ensemble clustering

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：