检索结果-内蒙古大学图书馆

2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ashraf, Humaira Malik, Javeria Ihsan, Uswa Al-Quayed, Fatima Humayun, Mamoona Jhanjhi, N.Z. International Islamic University Department of Computer Science and Software Engineering Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Taylor's University Malaysia Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

In era of modern world and technology where everything is digitalized. Cloud computing emerged as one of the fastest growing parts of digital industry. With the Succor of cloud computing models, business accomplishes their objectives with least exertion when contrasted with traditional processing environment. In spite of these compensations, yet security of the information in cloud database is the significant fear in endorsement of cloud. Although several techniques have been proposed such as cryptographic and steganographic approaches, but all of these have some loopholes. Therefore, in this paper new data protection technique based on steganographic encryption is proposed which has less computational time and secure against known plain text and Man-In-Middle Attack. © 2023 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Simulation of Subgouge Sand Deformations Using Robust Machine Learning Algorithms

Simulation of Subgouge Sand Deformations Using Robust Machin...

引用

Offshore Technology Conference, OTC 2022

作者： Azimi, Hamed Shiri, Hodjat Mahdianpari, Masoud Civil Engineering Department Faculty of Engineering and Applied Science Memorial University of Newfoundland Canada Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland Canada

ISBN: (纸本)9781613998526

Ice gouging is one of the major menaces to the subsea pipelines crossing the Arctic (e.g., Beaufort Sea) or the non-Arctic (e.g., Caspian Sea) shallow waters. Burial of the sea-bottom-founded infrastructures is regarded as a feasible method for protection of the subsea assets against the ice gouging threat. These pipelines are commonly embedded underneath the deepest ice-scoured records in the area, whereas the pipeline system is still threatened by the ice-induced soil displacement developed into the ice tip owing to the shear resistance of the seabed soil. Determination of the sub-gouge soil displacements is a governing design factor for the subsea structures in the Arctic offshore that commonly need costly laboratory studies and long-running finite element (FE) analyses to guarantee the operational integrity of the subsea pipeline against the ice-gouging event. Thus, the industry is still seeking more cost-effective, reliable, and faster alternative approaches for simulation of the iceberg-seabed-pipeline interaction process to minimize the collision risk of ice keels with the subsea structures. Recently, the application of machine learning (ML) in different fields has witnessed impressive growth since the ML technology is sufficiently precise, quick, reliable, and cost-effective to model various linear and non-linear problems. In this study, three robust ML algorithms comprising the Decision Tree Regression (DTR), Random Forest Regression (RFR), and Extra Tree Regression (ETR) models were used for the first time to simulate the iceberg-seabed interaction process in the sandy seabed. Using the parameters governing the ice-seabed interaction mechanism, a set of the DTR, RFR, and ETR models were developed. To verify the ML models, a comprehensive dataset was constructed and the data was divided into two sub-samples including the training (70% of data) and testing (the remaining 30% of the data) datasets. Subsequently, for the DTR, RFR, and ETR models, several analy

关键词： Uncertainty analysis

来源：评论

学校读者我要写书评

暂无评论

Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

引用

computers, Materials & Continua 2022年第2期70卷 2097-2111页

作者： R.H.Aswathy P.Suresh Mohamed Yacin Sikkandar S.Abdel-Khalek Hesham Alhumyani Rashid A.Saeed Romany F.Mansour Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare *** gadgets generate huge volumes of patient data in healthcare domain,which can be examined on cloud over the available storage and computation resources in mobile *** Kidney Disease(CKD)is one of the deadliest diseases that has high mortality rate across the *** current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm(FPA)-based Deep Neural Network(DNN)model abbreviated as *** steps involved in the presented FPA-DNN model are data collection,preprocessing,Feature Selection(FS),and ***,the IoT gadgets are utilized in the collection of a patient’s health *** proposed FPA-DNN model deploys Oppositional Crow Search(OCS)algorithm for FS,which selects the optimal subset of features from the preprocessed *** application of FPA helps in tuning the DNN parameters for better classification *** simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD *** results were examined under different *** simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%,specificity of 98.66%,accuracy of 98.75%,F-score of 99%,and kappa of 97.33%.

关键词： Deep learning chronic kidney disease IoT cloud computing feature selection classification

来源：评论

学校读者我要写书评

暂无评论

Intelligent Caching Strategies for 5G Edge Networks using Machine Learning 4

Intelligent Caching Strategies for 5G Edge Networks using Ma...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Guduri, Naga Venkata Ramakrishna Sethu, S. Shalinirajan, R. Reena, R. Karthikeyan, M. Uthayakumar, G.S. University of Technology and Applied Sciences-Al Musannah Electrical and Electronics Section Engineering Department Oman Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Computer Science and Business System Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%. © 2023 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

Graph-based deep learning models in the prediction of early-stage Alzheimers 46

Graph-based deep learning models in the prediction of early-...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Thapaliya, Bishal Wu, Zundong Sapkota, Ram Ray, Bhaskar Suresh, Pranav Ghimire, Santosh Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States Tribhuvan University Department of Applied Sciences and Chemical Engineering Nepal

ISBN: (纸本)9798350371499

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Iceberg draft prediction using gradient boosting regression algorithm

引用

Marine Systems and Ocean Technology 2024年第3-4期18卷 151页

作者： Azimi, Hamed Shiri, Hodjat Mahdianpari, Masoud Civil Engineering Department Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNLA1B 3X5 Canada Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNLA1B 3X5 Canada C-CORE 1 Morrissey Rd St. John’sNLA1B 3X5 Canada

The Arctic area is one of the best destinations for the development of oil and gas loading equipment. However, the recent development of oil and gas facilities, including the submarine pipelines and wellheads crossing the Arctic area, has elevated the need for more attention to iceberg draft (under-water height of icebergs) estimation during an ice scouring event. This means if the draft of an iceberg is more than the ocean deepness, the iceberg tip can gouge the ocean floor and collide with the subsea assets;as a result, the operational integrity of the submarine infrastructures is threatened. Hence, the estimation of the iceberg drafts is vital for the oil and gas operators in the Arctic waters. In the present study, the use of the Gradient Boosting Regression (GBR) algorithm was proposed to simulate the iceberg drafts for the first time. Initially, the parameters governing the iceberg drafts were recognized, and nine GBR models were then developed. The premium GBR model along with the most important inputs was known by conducting a sensitivity analysis, e.g., the value of correlation coefficient (R) and Akaike Information Criteria (AIC) for the premium GBR model was obtained at 0.907 and 12.571, while the iceberg length ratio (L⁄H) and iceberg width ratio (W/ H) were recognized as the most influential input parameters to predict the iceberg drafts. The best GBR model was compared with Support Vector Regression (SVR) and K-Nearest Neighbors Regression (KNR) algorithms, rather the GBR algorithm had the highest degree of accuracy and correlation as well as the lowest amount of complexity. The error analysis demonstrated that roughly 34% of the iceberg draft predicted by the premium GBR model had an error of less than 10%;however, this value for the SVR and KNR models was almost 19%. Lastly, the performed uncertainty analysis proved that the superior GBR model possessed the narrowest bound of uncertainty, with an overestimated performance in the iceberg draft simulat

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

MEMS-based twisted bilayer flat optics for multidimensional optical modulation and hyperimaging 14

MEMS-based twisted bilayer flat optics for multidimensional ...

引用

High Contrast Metastructures XIV 2025

作者： Tang, Haoning Lou, Beicheng Du, Fan Gao, Guangqi Zhang, Mingjie Ni, Xueqi Yacoby, Amir Cao, Yuan Fan, Shanhui Mazur, Eric School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States

Our MEMS-based twistoptics device enables precise control of interlayer gaps and twist angles in photonic crystals, offering high-accuracy, multidimensional light manipulation. This finding holds potential for applica... 详细信息

ISBN: (纸本)9781510685048

关键词： Nanophotonics

来源：评论

学校读者我要写书评

暂无评论

Coupled effects of surface elasticity, couple stresses, and adhesion in nanocontact mechanics

引用

applied mathematics and Mechanics(English Edition) 2025年第07期46卷 1237-1260页

作者： Youxue BAN Xinyao YANG Q.X.LI Changwen MI Jiangsu Key Laboratory of Mechanical Analysis for Infrastructure and Advanced Equipment School of Civil Engineering Southeast University Department of Applied Mathematics Xi'an Jiaotong Liverpool University Department of Mathematics and Computer Science Fisk University

This paper investigates the adhesive nanocontact behavior of an elastic halfplane indented by a rigid cylindrical indenter, incorporating the simultaneous effects of surface elasticity, couple stresses, and adhesion. The free surface of the half-plane is modeled by the Steigmann-Ogden surface elasticity theory, while the bulk material behavior is described by the classical couple-stress elasticity theory. The adhesion at the contact interface is characterized by the Maugis-Dugdale(MD) adhesive contact model. Building on the fundamental nonclassical Flamant solution, the governing equations and boundary conditions of the nanocontact problem are reformulated into a system of triple integral equations. These equations are solved numerically by the Gauss-Chebyshev quadratures in combination with an iterative algorithm. The validation against the existing literature confirms the accuracy and robustness of the proposed solution methodology. Comprehensive parametric studies are performed to elucidate the critical roles of surface elasticity and couple stresses in adhesive nanocontact. The numerical results provide insights into the complex interactions among surface, couple-stress, and adhesive effects. Specifically, the interplay between the surface and adhesive effects is predominantly competitive, while the interaction between the couple stresses and adhesion exhibits an intricate *** findings highlight the necessity of simultaneously considering surface elasticity, couple stresses, and adhesion in nanoindentation analyses to achieve accurate predictions of material responses.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Anonymous IoT-Based E-Health Monitoring System Using Blockchain Technology

引用

IEEE Systems Journal 2023年第2期17卷 2422-2433页

作者： Samuel, Omaji Omojo, Akogwu Blessing Mohsin, Syed Muhammad Tiwari, Prayag Gupta, Deepak Band, Shahab S. Department of Computer Science Osara264103 Nigeria Edo State University Uzairue300281 Nigeria Shestco Applied Mathematics and Simulation Advanced Research Centre Abuja186 Nigeria Comsats University Islamabad Department of Computer Science Islamabad45550 Pakistan Aalto University Department of Computer Science Espoo02150 Finland Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi110086 India Research Advisor Ucrd Chandigarh University GharuanPunjab Mohali140413 India National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan

The Internet of Things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive, and real-time decision-making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this article proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust, and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchain-based coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest consensus protocol has 15.93% less computational cost than 26.30% of proof-of-work and 57.77% proof-of-authority consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology. © 2007-2012 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

A Study of Different Machine Learning Approaches for Relation Discovery Task

A Study of Different Machine Learning Approaches for Relatio...

引用

2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： AL-Aswadi, Fatima N. Za’al Alma’aitah, Wafa Chan, Huah Yong Gan, Keng Hoon Iqbal, Zahid Al-Dhamari, Ahlam Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Faculty of Computer Science and Engineering Hodeidah University P.O. Box 3114 Hodeidah Yemen Department of Intelligence Systems Faculty of Artificial Intelligence Al-Balqa Applied University Salt19117 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia Department of Computer Science Air University Kharian Pakistan Department of Computer Science University of Gujrat Gujrat Pakistan Department of Electrical and Computer Engineering Curtin University Malaysia Sarawak Miri98009 Malaysia

ISBN: (纸本)9781837243235

Relation extraction and classification, collectively referred to as relation discovery, are fundamental tasks in the ontology construction process. In recent years, numerous studies have dedicated their efforts to devising hybrid approaches, supporting inclusive innovation in computational linguistics and ontology engineering. These approaches merge traditional methods with Machine Learning (ML) or Deep Learning (DL) techniques to tackle relation discovery tasks. This research examines the applicability and efficacy of four distinct models— Support Vector Machine (SVM), Naive Bayes (NB), k-Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—in relation extraction and classification. Two different datasets, ACL RD-TEC 2.0 and ACLRelAcS, are utilized, alongside two distinct text representation methodologies, BOW and TF-IDF-BOW. An in-depth analysis of the results, including the advantages and limitations of each model, is presented in the results and discussion sections. © The Institution of engineering & Technology 2025.

关键词： Deep learning

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：