检索结果-内蒙古大学图书馆

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

作者： Goswami, Veena Sharma, Bhisham Patra, Sudhansu Shekhar Chowdhury, Subrata Barik, Rabindra Kumar Dhaou, Imed Ben School of Computer Applications KIIT Deemed to Be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MCA Department AP Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

In the current scenarios there is a lot of development in the networking sector. Additionally needed are quick operations and the capability to solve complex issues. From several technical angles, IoT is being promoted to meet these developments. Implementations of IoT confront difficulties in handling such enormous volume of data including issues with its Quality of Services (QoS) necessities, privacy with security and the variety of networking elements. In smart cities, a vast volume of data is generated through IoT devices. They need to be processed near the edge devices due to latency issues, especially in the case of critical data-sensitive applications. Fog computing, a new technological paradigm, delivers a collection of networking essential services nearer to the client than cloud computing does. Fog computing overrides the cloud computing in the areas such as networking infrastructure scalability, latency reduction, network service dependability, and network device security. The technical approach to provide the highest degree of computing service has advanced by contributing cloud-assisted network services nearer to the end user/customer level. Fog servers can malfunction in a variety of circumstances. In this study, we describe the fog system as a machine-repair problem, where repair work is performed at a certain pace as soon as the Virtual Machine (VM) malfunctions. To analyze the system, numerous numerical analyses have been conducted. © 2023 IEEE.

关键词： Fog computing

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

(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Quality Control

(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Qual...

引用

International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Fotios Kalioras Gabriel Filios Stylianos Karatzas Sotiris Nikoletseas Department of Computer Engineering and Informatics University of Patras Greece Department of Computer Engineering and Informatics University of Patras Computer Technology Institute and Press “Diophantus” Greece Department of Civil Engineering University of Patras Greece

Indoor air quality (IAQ) is an important yet often overlooked aspect of public health, with poor IAQ contributing to a significant number of diverse health problems worldwide. Existing air quality standards have failed to fully address the problem, however emerging technologies in the field of IoT have demonstrated promise in effectively addressing this issue. This concept paper advocates a hybrid hierarchy system for IAQ monitoring that incorporates both offline and online connectivity. The system utilizes sensing elements that can perform a wide range of functions, including autocalibration, pollution event detection, and more, autonomously from the network. By combining a gateway device, the system's capabilities are enhanced, providing increased data granularity, additional calibration and sensing options as well as connection with Building Management Systems (BMS). Furthermore, by connecting the gateway to the cloud, the system can perform more advanced data analysis and machine learning, providing users with insights into not only air quality but also general environmental quality. Key features of the envisioned system include the integration of subjective perception of pollution via crowdsourcing, a TinyML technology at the edge, digital twins, and an optimised redundancy of sensors that can alleviate poor accuracy and drift as well as capture the spatial dispersion of pollution.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Multi-Contrast Dixon MRI and CT for Abdominal Fat and Muscle Segmentation Using a UNet CNN

Performance Evaluation of Multi-Contrast Dixon MRI and CT fo...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Heller, Andrew Liu, Andrew Chang, Lin-Ching Cohen, Gregg Jones, Elizabeth C. Hsu, Li-Yueh National Institutes of Health Clinical Center BethesdaMD United States Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States

ISBN: (纸本)9798350362480

We evaluate the performance of a deep learning framework for segmenting abdominal fat and muscle using multi-contrast Dixon magnetic resonance (MR) and computed tomography (CT) images. We aim to compare MR image segmentation by testing Dixon fat-only, water-only, and combination of both types of images and comparing the results with CT images. Nineteen subjects underwent abdominal CT and Dixon MR imaging on the same day. For each participant, three pairs of matched axial images from both CT and MR were selected at the intervertebral levels of L2-L3, L3-L4, and L4-L5 for analysis. References labels were generated through semi-automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and muscle areas. They were then used to train and evaluate a U-Net based Convolutional Neural Network (CNN) framework with a 3-fold cross-validation to compare the segmentation performance across CT, Dixon fat-only and water-only MR images. Combining the fat-only and water-only MR image inputs produced superior results in all labels. Our study demonstrates that CNN-based segmentation performance for abdominal fat and muscle improves with the inclusion of additional input channels, such as combining Dixon fat-only and water-only MR images. While CT results represent the gold standard in abdominal image segmentation, increasing the number of input image channels used in MR segmentation can approach, and even match, the results of CT. © 2024 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Tweet Analysis Algorithms Using Natural Language Processing and Machine Learning Models 18

A Comparative Analysis of Tweet Analysis Algorithms Using Na...

引用

18th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2023

作者： Kanavos, Andreas Antonopoulos, Nikos Karamitsos, Ioannis Mylonas, Phivos Ionian University Department of Informatics Corfu Greece Ionian University Department of Digital Media and Communication NeMeCULAB Argostoli Greece Rochester Institute of Technology Department of Graduate and Research Dubai United Arab Emirates University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350327717

Online Social Networks (OSNs) have become integral platforms for information sharing, attracting both legitimate users and spammers. Detecting and mitigating spam within these networks pose significant challenges due to the dynamic nature of content and user behavior. In this paper, we present a comprehensive comparative analysis of algorithms for tweet analysis, focusing on Natural Language Processing (NLP) and Machine Learning (ML) models. We evaluate these algorithms through sentiment analysis and multiple attribute analysis, utilizing diverse methodologies and datasets. Our study explores feed-forward neural networks, Bayesian classifiers, and transformer-based models for NLP tasks, achieving high prediction accuracy and insightful metrics such as precision, recall, and F1 score. Furthermore, we delve into multiple attribute analysis using Random Forest, Logistic Regression, and Gradient Boosting algorithms. Through a systematic exploration of various approaches, this work contributes to a deeper understanding of spam detection and sentiment analysis within the context of OSNs, paving the way for enhanced social network security and content analysis. © 2023 IEEE.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

引用

computers, Materials & Continua 2022年第2期70卷 2761-2775页

作者： Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Faculty of Engineering Port Said UniversityPort Fuad CityEgypt

In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is why an automated weapon detection system is *** automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good ***,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection *** models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance *** research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key *** proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur *** proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 *** results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are *** reached 0.010 s per frame compared to the 0.17 s of the Faster *** is promising to be used in the field of security and weapon detection.

关键词： Video surveillance weapon detection you only look once convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Approximate EFX and Exact tEFX Allocations for Indivisible Chores: Improved Algorithms

arXiv

引用

arXiv 2024年

作者： Afshinmehr, Mahyar Ansaripour, Matin Danaei, Alireza Mehlhorn, Kurt Department of Computer Engineering Sharif University of Technology Iran Computer and Communications Sciences Department EPFL Switzerland Max Planck Institute for Informatics SIC Germany

We explore the fair distribution of a set of m indivisible chores among n agents, where each agent’s costs are evaluated using a monotone cost function. Our focus lies on two fairness criteria: envy-freeness up to any item (EFX) and a relaxed notion, namely envy-freeness up to the transfer of any item (tEFX). We demonstrate that a 2-approximate EFX allocation exists and is computable in_polynomial time for three agents with subadditive cost functions, improving upon the previous (2 + √6) approximation for additive cost functions. This result requires extensive case analysis. Christoforidis and Santorinaios (2024) independently claim the same approximation for additive cost functions;however, we provide a counter-example to their algorithm. We expand the number of agents to any number to get the same approximation guarantee with the assumption of partially identical ordering (IDO) for the cost functions. Additionally, we establish that a tEFX allocation is achievable for three agents if one has an additive 2-ratio bounded cost function, while the others may have general monotone cost functions. This is an improvement from the prior requirement of two agents with additive 2-ratio bounded cost functions. This allocation can also be extended to agent groups with identical valuations. Further, we show various analyses of EFX allocations for chores, such as the relaxations for additive α-ratio-bounded cost functions. © 2024, CC BY-NC-SA.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

A Contend Node-Restricted Receiver-Initiated MAC Protocol With Consecutive Packet Transmission

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Yuxin Ota, Kaoru Dong, Mianxiong Wang, Ruoyu Changsha University College of Computer Science and Engineering Changsha410022 China Muroran Institute of Technology Department of Sciences and Informatics Japan Central South University School of Computer Science and Engineering Changsha410083 China

The medium access control (MAC) protocol plays a crucial role in wireless communications, influencing key performance metrics, such as energy consumption, delay, collision, and throughput. This paper introduces the Contend Node Restricted Joint Consecutive Packet Transmissions Receiver-Initiated (CNR-CPT-RI) protocol, specifically designed to minimize collisions and reduce energy consumption in wake-up radio-enabled wireless sensor networks (WSNs). The CNR-CPT-RI protocol is an effective communication protocol that is mainly applied with nodes in the one-hop range of the sink. Since the sink has the ability to control each node, it becomes feasible to manage the nodes in a way that effectively reduces collisions and enhances network lifetime as well as throughput. Moreover, the CNR-CPT-RI protocol can adaptively adjust the number of nodes participating in channel contention, maintaining it within an optimal range to balance reduced collisions, decreased energy consumption, and sustained high throughput. Furthermore, the winning node in channel contention employs Consecutive Packet Transmissions (CPTs) method to further diminish competition. Comprehensive theoretical and experimental results demonstrate that the number of collisions of the CNR-CPT-RI protocol is only 42.4%, 35.9%, and 32.5% of the baseline protocol when the number of nodes is 10, 20, and 30, respectively. The energy consumption of the CNR-CPT-RI protocol is reduced by 5.7%, 15%, and 22.6%, and the delay is decreased by 6.7%, 16%, and 32%, respectively, compared with that of the existing protocol. © 2017 IEEE.

关键词： Internet protocols

来源：评论

学校读者我要写书评

暂无评论

Leveraging LLM in Genetic Programming Hyper-heuristics for Dynamic Microservice Deployment 37th

Leveraging LLM in Genetic Programming Hyper-heuristics for...

引用

37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Wang, Chen Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany National Institute of Water and Atmospheric Research Wellington New Zealand

ISBN: (纸本)9789819603473

Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., minimizing Energy Consumption (EC) vs. minimizing Communication Overhead (CO). Recently, some hyper-heuristics methods, particularly Genetic Programming Hyper-Heuristics (GPHH), have been proposed to automatically generate heuristics for solving dynamic microservice deployment problems. Meanwhile, Large Language Models (LLMs) are becoming popular for solving various domain-specific problems thanks to their strong ability to learn problem-related knowledge. However, hybridizing GPHH with LLM by combining their abilities in solving complex optimization problems remains unexplored. In this paper, we propose an LLM-enhanced Genetic Programming Hyper-Heuristic (LLM-GPHH) algorithm to evolve heuristics for the dynamic deployment of applications composed of microservices, to jointly optimize EC and CO. Our experiments on real-world datasets demonstrate the effectiveness of the newly proposed LLM-GPHH. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

Missing Data Imputation: A Comprehensive Review

引用

Journal of computer and Communications 2024年第11期12卷 53-75页

作者： Majed Alwateer El-Sayed Atlam Mahmoud Mohammed Abd El-Raouf Osama A. Ghoneim Ibrahim Gad Department of Computer Science College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Basic and Applied Science Institute College of Engineering and Technology Arab Academy for Science and Technology (AAST) Alexandria Egypt Department of Computer Science Faculty of Computers and informatics Tanta University Tanta Egypt

Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.

关键词： Missing Data Machine Learning Prediction Deep Learning Imputation

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：