检索结果-内蒙古大学图书馆

Mitigating Urban Heat Island Effects: A Review of Innovative Pavement Technologies and Integrated Solutions

Structural Durability & Health Monitoring 2024年第5期18卷 525-551页

作者： S.F.Ismael A.H.Alias N.A.Haron B.B.Zaidan Abdulrahman M.Abdulghani Department of Civil Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Central Region Heavy Civil DivisionGranite ConstructionWoodlandsTexas77381USA SP Jain School of Global Management LidcombeSydneyNSW2141Australia Department of Communication Technology and Network Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdang43400Malaysia

In this review paper,we present a thorough investigation into the role of pavement technologies in advancing urban *** analysis traverses the historical evolution of these technologies,meticulously evaluating their socio-economic and environmental impacts,with a particular emphasis on their role in mitigating the urban heat island *** evaluation of pavement types and variables inﬂuencing pavement performance to be used in the multi-criteria decision-making(MCDM)framework to choose the optimal pavement application are at the heart of our *** serves to assess a spectrum of pavement options,revealing insights into the most effective and sustainable *** highlighting both the existing challenges and potential innovative solutions within theﬁeld,this paper aims to offer a directional compass for future urban planning and infrastructural *** review not only synthesizes the current state of knowledge but also aims to chart a course for future exploration,emphasizing the critical need for innovative and environmentally sensitive pavement tech-nologies in the creation of resilient and sustainable urban environments.

关键词： Pavement sustainability urban heat island environmental impact of pavements innovative pavement solutions economic and social implications sustainable development in urban areas

来源：评论

学校读者我要写书评

暂无评论

Lightweight Adaptive Learning Algorithm for Energy-Latency Tradeoff in IoMT-Enabled Edge Computing 17

Lightweight Adaptive Learning Algorithm for Energy-Latency T...

引用

17th International Conference on Machine Vision, ICMV 2024

作者： Yadav, Rahul Shafiq, Muhammad Kumar, Mohit Wei, Li Elaziz, Mohamed Abd College of Computer Science and Technology Harbin Engineering University Harbin China Cyberspace Institute of Advance Technology Guangzhou University Guangzhou China Department of Information Technology National Institute of Technology Jalandhar India Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt

ISBN: (纸本)9781510688278

Smart Healthcare is witnessing widespread implementation of Internet-of-Medical-Things (IoMT) devices. These devices play a crucial role in collecting vast amounts of data from various smart healthcare applications, which are then processed to facilitate informed decision-making. Edge computing has emerged as a valuable platform offering computational resources to handle this data collection efficiently. However, a critical challenge arises from the inappropriate and inefficient classical approaches to fair resource allocation in these energy-intensive, short battery life and delay-intolerant portable devices. To address this issue, this paper presents a Lightweight Adaptive Learning Offloading Algorithm designed to optimize energy and latency in an edge computing environment. The proposed algorithm formulates the problem as a combined minimization of latency and energy costs while satisfying constraints related to limited battery capacity and service latency deadlines. To achieve this, the algorithm employs the adaptation parameters (α, β, γ), and the tradeoff target (C′) is configured based on the specific task requirements. Experimental results demonstrate the advantages of the proposed scheme, including energy savings and minimized latency in an edge environment. To validate the effectiveness of the proposed scheme under a realistic scenario using the iFogSim2 simulator. © 2025 SPIE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Secret Key Optimization for Secure Speech Communications

引用

computers, Materials & Continua 2022年第8期72卷 3025-3037页

作者： Osama S.Faragallah Mahmoud Farouk Hala S.El-Sayed Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom 32511Egypt

This paper answers three essential questions for audio speech cryptosystems in time and discrete transform *** first question is,what are the best values of sub-keys that must be used to get the best quality and security for the audio cryptosystem in time and discrete transform *** second question is the relation between the number of sub-keys,the number of secret keys used,and the audio speech signal block’s ***,how many possible secret keys can be used to get the best quality and security results for the audio speech cryptosystem in time and discrete transform *** audio cryptosystem discussed before in recent research is applied to answer the three previous *** simulation results and analysis answer all three questions;first,there is no specific,well-defined format or rule for sub-key values that must be used to get a better *** the second question,yes,there is a direct relationship between the number of applicable keys,number of available sub-keys,and block size of speech audio signal and formulated into a first-order *** the third question,each discrete transform domain has a specific acceptable range of sub-keys that imply a particular number of keys that can be used to get a better cryptosystem.

关键词： Audio cryptosystem chaotic baker map secret key size

来源：评论

学校读者我要写书评

暂无评论

The Impact of Varying Batch Sizes on the Deep Neural Network Algorithms Performance 8

The Impact of Varying Batch Sizes on the Deep Neural Network...

引用

8th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2024

作者： Muharakah, Naemah Sihombing, Poltak Efendi, Syahril Fahmi, Fahmi Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan20155 Indonesia Universitas Sumatera Utara Department of Electrical Engineering Medan20155 Indonesia

ISBN: (纸本)9798331531249

Batch size is one of the determining factors in the quantity of sample data used in one iterative model training in deep learning. One of the key factors influencing the effectiveness of hyperparameters and model training results. Group size is very important in selection because the right group size will influence the effectiveness and performance of the training. Choosing a batch size that is too small or large can affect accuracy and performance. Deep Neural Network (DNN) algorithm is a form of artificial neural network consisting of several deep layers. There are several batch sizes used in data processing. Optimal performance for batch size is obtained when using a batch size of 64, as it yields the highest average AUC value of 0.815. Furthermore, the optimal configuration was achieved with a batch size of 64 and 6 hidden layers, utilizing a combination of Leaky-ReLV and Sigmoid activation functions, resulting in an AUC value of 0.83. © 2024 IEEE.

关键词： Activation Function Area Under the Curve Batchsizes Deep Neural Network Hidden Layer

来源：评论

学校读者我要写书评

暂无评论

Need of Public-Private Healthcare Collaboration for Managing Seasonal Dengue Fever in West Bengal 22

Need of Public-Private Healthcare Collaboration for Managing...

引用

22nd IEEE/ACIS International Conference on software engineering Research, Management and Applications, SERA 2024

作者： Nag, Anwesha Goto, Takaaki Roy, Subhankar Ghosh, Partha Adamas University Kolkata India Toyo University Faculty of Information Sciences and Arts Saitama Japan Academy of Technology Department of Computer Science and Engineering Adisaptagram India

ISBN: (纸本)9798350391343

Dengue fever is mostly prevalent in tropical and subtropical regions, where Aedes mosquitoes, the primary vectors for the virus, thrive in warm and humid environments. In West Bengal, a province in India, the typical duration of the dengue disease spans two to three months, necessitating substantial infrastructure for dengue patients during this period. If the government heavily invests in developing this infrastructure, there's a risk of these facilities remaining underutilized during periods of low dengue incidence. Conversely, without adequate investment, dengue could potentially escalate into an epidemic. This research seeks to identify regions where insufficient infrastructure impedes public healthcare systems from catering to dengue patients. The primary focus of this paper is to evaluate the necessary degree of public-private collaborations needed to address seasonal dengue epidemics and pinpoint specific durations within the healthcare system that require attention. © 2024 IEEE.

关键词： Investments

来源：评论

学校读者我要写书评

暂无评论

When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

引用

Green Energy and Intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model Intelligent transportation K-means

来源：评论

学校读者我要写书评

暂无评论

Renewable Energy Efficiency of Unmanned Aerial Vehicles Operating with Wireless Sensor Networks and Mobile Ad Hoc Networks 2

Renewable Energy Efficiency of Unmanned Aerial Vehicles Oper...

引用

2nd International Conference on Smart Technologies for Power and Renewable Energy, SPECon 2024

作者： Ahmad, Wakeel Ahmed, Sarfaraz Ahmad, Adeel Siddiqui, Shams Tabrez Khamruddin, Mohammad Khan, Haneef Jazan University College of Applied Industrial Tech Deptartment of Electrical Engineering Technology Jazan Saudi Arabia Jazan University College of Computer Science and Information Technology Dept. of Cnet Jazan Saudi Arabia Aligarh Muslim University Faculty of Science Department of Computer Science U.P Aligarh India Jazan University College of Computer Science and Information Technology Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798350383041

The amalgamation of Unmanned Aerial Vehicles (UAVs) into Wireless Sensor Networks (WSNs) and Mobile Ad Hoc Networks (MANETs) provides significant prospects to improve network capabilities. This study examines the energy renewable efficiency and performance of UAVs operating in WSNs and MANETs. The assessment considers a variety of elements that influence energy consumption, including the possibility of integrating renewable energy, energy management measures, and the impact on overall network performance. This seeks to provide insights into optimizing energy usage and improving the operating efficiency of UAVs in these dynamic network situations through extensive analysis and evaluation. A special emphasis is placed on the management of mobility, the optimization of energy usage, and the effectiveness of routing protocols. The paper explores existing protocols such as LEACH, AODV and OLSR highlighting their benefits and challenges for UAV's. A comparative analysis evaluates adapted protocols based on metrics such as energy efficiency, latency, and reliability. © 2024 IEEE.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

引用

computer Systems science & engineering 2023年第12期47卷 2953-2969页

作者： Ehab Bahaudien Ashary Sahar Jambi Rehab B.Ashari Mahmoud Ragab Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Object Detection is the task of localization and classification of objects in a video or *** recent times,because of its widespread applications,it has obtained more *** the modern world,waste pollution is one significant environmental *** prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher *** object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or *** technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental *** studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data ***,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)*** presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste *** the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object *** object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone *** addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are ***,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste *** performance validation of the EFFDL-WODC approach was validated on the benchmark *** comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

关键词： Object detection object classification waste management deep learning feature fusion

来源：评论

学校读者我要写书评

暂无评论

Wireless modulation classification based on Radon transform and convolutional neural networks

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 6263-6272页

作者： Ghanem, Hanan S. Al-Makhlasawy, Rasha M. El-Shafai, Walid Elsabrouty, Maha Hamed, Hesham F. A. Salama, Gerges M. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia61111 Egypt Department of Computers and Systems Electronics Research Institute Cairo Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electronics and Communications Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Convolutional Neural Networks (CNNs) are efficient tools for pattern recognition applications. They have found applications in wireless communication systems such as modulation classification from constellation diagrams. Unfortunately, noisy channels may render the constellation points deformed and scattered, which makes the classification a difficult task. This paper presents an efficient modulation classification algorithm based on CNNs. Constellation diagrams are generated for each modulation type and used for training and testing of the CNNs. The proposed work depends on the application of Radon Transform (RT) to generate more representative patterns for the constellation diagrams to be used for training and testing. The RT has a good ability to represent discrete points in the spatial domain as curved lines. Several pre-trained networks including AlexNet, VGG-16, and VGG-19 are used as classifiers for modulation type from the spatial-domain constellation diagrams or their RTs. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different Signal-to-Noise Ratios (SNRs) and fading channel conditions. © 2022, The Author(s).

关键词： Fading channels

来源：评论

学校读者我要写书评

暂无评论

Topic Modeling and Sentiment Analysis of US’ Afghan Exit Twitter Data: A Text Mining Approach

引用

International Journal of information and Management sciences 2023年第1期34卷 51-64页

作者： Clement, Addo Prince Kofi, Akpatsa Samuel Maxwell, Dorgbefu Jr Caleb, Dagadu Joshua Dela, Tattrah Victor Kofi, Dzoagbe Newton Dodzi, Fiawoo Duncan Juliana, Nartey Department of Information Technology Education Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Ghana Department of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Public Affairs and Administration University of Electronic Science and Technology of China Department of Mathematics and Computer Studies Jasikan College of Education University of Cape Coast Ghana University of Cape Coast Ghana

The withdrawal of US troops from Afghanistan and the sub-sequent collapse of the Afghan government threatens the lives, security, and human right of many people. Twitter and other social media platforms took the lead in opinion and sentiment sharing, allowing researchers to make a real-time assessment that may help authorities develop early response strategies. In this study, 362,566 tweets relating to the exit of the US troops from Afghanistan collected between August 11 and 27, 2021, are analyzed using text mining techniques, including sentiment analysis and word cloud analysis. The analysis shows diverse topics on social media related to the fallout, and the general reaction regarding the crisis was negative. © 2023, Tamkang University. All rights reserved.

关键词： Sentiment analysis

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：