检索结果-内蒙古大学图书馆

3-Multi ranked encryption with enhanced security in cloud computing

Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio Artificial Intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of Computer Science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and Computer Science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Dept.of Computer Science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when *** refer to a model that satisfies all of the conditions a 3-multi ranked search ***,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation *** is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real *** this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security *** proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same ***,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different *** evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

来源：评论

学校读者我要写书评

暂无评论

Multi-View Dynamic Kernelized Evidential Clustering

引用

IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

来源：评论

学校读者我要写书评

暂无评论

ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

引用

Computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

来源：评论

学校读者我要写书评

暂无评论

How AI-enabled SDN technologies improve the security and functionality of industrial IoT network:Architectures,enabling technologies,and opportunities

引用

Digital Communications and Networks 2023年第6期9卷 1351-1362页

作者： Jinfang Jiang Chuan Lin Guangjie Han Adnan MAbu-Mahfouz Syed Bilal Hussain Shah Miguel Martínez-García Department of Internet of Things Engineering Hohai UniversityChangzhou213022China Software College Northeastern UniversityShenyang110819China Council for Scientific and Industrial Research(CSIR) Pretoria0184South Africa School of Computing and Mathematics Manchester Metropolitan UniversityManchesterM156BHUK Department of Aeronautical and Automotive Engineering Loughborough UniversityLoughboroughLE113TTUK Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburgSouth Africa

The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communication *** brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face,providing ad-hoc functions for scheduling and guaranteeing the network ***,the large development of softwareDefined Networking(SDN)and Artificial Intelligence(AI)technologies have made feasible the design and control of scalable and secure IIoT *** paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT *** surveying the state-of-the-art research efforts in the subject,the paper introduces a candidate architecture for AI-enabled software-Defined IIoT Network(AI-SDIN)that divides the traditional industrial networks into three functional *** with this aim in mind,key technologies(Blockchain-based Data Sharing,Intelligent Wireless Data Sensing,Edge Intelligence,Time-Sensitive Networks,Integrating SDN&TSN,Distributed AI)and improve applications based on AISDIN are also ***,the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.

关键词： Industrial internet of things(IIoT) Industry 4.0 Artificial intelligence(AI) Machine intelligence software-defined networking(SDN)

来源：评论

学校读者我要写书评

暂无评论

Efficient Prediction of Protein-Ligand Binding Using ESM-2 and Mol2vec with Random Forest Model

Efficient Prediction of Protein-Ligand Binding Using ESM-2 a...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Shrestha Gurung, Bichar Dip Rayamajhi, Manish Baidya, Anushuya Growney, Owen Dongmo Mafo, Helena Naomie Choi, Yun-Seok Gnimpieba, Etienne Z. University of South Dakota Dept. of Biomedical Engineering VermillionSD United States Black Hills State University School of Natural Sciences SpearfishSD United States Kennesaw State University College of Computing and Software Engineering MariettaGA United States

ISBN: (纸本)9798350386226

In this study, we present a predictive model for protein-ligand binding interactions, leveraging ESM-2 and Mol2vec embeddings combined with a Random Forest classifier. Our approach was evaluated across three testing scenarios - "Molecule unseen,""Protein unseen,"and "None seen"- to assess its generalizability to novel compounds and targets. Compared to deep learning models, our method demonstrated competitive AUROC scores and consistently high specificity, indicating robust predictive performance and a conservative strategy that minimizes false positives. While sensitivity was lower, strategies to address data imbalance and enhance model sensitivity are discussed. Our findings highlight the potential of combining efficient embedding techniques with interpretable machine learning models for scalable and effective binding interaction prediction © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

An Explainable Counterfeit and Genuine Ethiopian Banknote Classification Using Deep Learning

SSRN

引用

SSRN 2024年

作者： Woldehana, Yared Dereje Yimer, Mohammed Abebe School of Computing and Informatics Dilla University Dilla Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

Counterfeiting is a serious crime with serious impact around the world and in Ethiopia in particular. The national bank of Ethiopia has implemented various countermeasures to combat counterfeiting. The most successful counterfeit banknote detectors in use today are cash counters, which are hardware-based systems that use optical and magnetic sensors to detect and validate banknotes. This technology entails a high cost and low availability for the public and small businesses, where we find the biggest cash circulation outside of banks. Existing countermeasures are insufficient to address this critical issue. Advancements in technology, such as digital printing and sophisticated scanning equipment, have made it easier for counterfeiters to mislead their victims by producing banknotes very similar to genuine ones. Only a handful of studies have been conducted on the current Ethiopian banknotes. This study presents an explainable deep learning-based model for the classification of genuine and counterfeit Ethiopian banknotes. The study used transfer learning with SHAP (Shapley Additive explanations) and TF-EXPLAIN (TensorFlow Explain) explainable artificial intelligence frameworks for a better understanding of the classification prediction behind the models. Experimental results show that DENSE121 achieved the best accuracy of 99.87%, and InceptionV3 achieved a very similar result of 99.50%. © 2024, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Metaverse with Mobile Twin Edge computing: Empowering Optimal Efficiency and Accuracy

Metaverse with Mobile Twin Edge Computing: Empowering Optima...

引用

2024 International Conference on engineering and computing, ICECT 2024

作者： Bibi, Asma Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Software Engineering Islamabad Pakistan Kohsar University Department of Computer Science Murree Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9798350349719

The concept of Digital Twin has been widely used by researchers to represent physical entities in computer-generated reality in the metaverse. In this research, a novel concept of 'Mobile Twin' is coined. Mobile Twin serves as a replica of a mobile device in the virtual environment enabling communication. The purpose of introducing Mobile Twin in the metaverse is to bring mobility and benefits similar to tangible mobile devices while eliminating the need for individual energy sources, storage limitations, resource usage, and efficiency. The true benefits of a mobile twin can be achieved with an architecture where computing nodes are decentralized, data processing is near the network's edge with faster response times and reduced latency. For this purpose, the mobile twin-based applications of metaverse are built over edge computing-based network architecture. This idea introduced the aspect of 'Revolutionary Ultra-low latency (RULL)' in the metaverse. © 2024 IEEE.

关键词： Electric power utilization

来源：评论

学校读者我要写书评

暂无评论

Observation points classifier ensemble for high-dimensional imbalanced classification

引用

CAAI Transactions on Intelligence Technology 2023年第2期8卷 500-517页

作者： Yulin He Xu Li Philippe Fournier‐Viger Joshua Zhexue Huang Mianjie Li Salman Salloum College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen UniversityShenzhenChina Faculty of Information Technology Macao University of Science and TechnologyMacaoChina School of Computing National University of SingaporeSingaporeSingapore

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction ***,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as ***,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data ***,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown *** experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data *** results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC *** demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

关键词： classifier ensemble feature transformation high-dimensional data classification imbalanced learning observation point mechanism

来源：评论

学校读者我要写书评

暂无评论

Loan Repayment Predictive Model Using Ensemble Algorithm

Loan Repayment Predictive Model Using Ensemble Algorithm

引用

SmartBlock4Africa, IEEE

作者： O. Godwin Akinbayo G. Akintola Kolawole B. Adewole David Department of Software Engineering School of Computing Federal University of Technology Akure Ondo State Nigeria

ISBN: (数字)9798350376838

ISBN: (纸本)9798350376845

The accurate prediction of loan repayment ability is paramount in any financial industry to minimize risks and enhance decision making. This project centers on the development of a loan repayment predictive model using ensemble algorithm. The performance of the algorithm is evaluated through the following metrics: f1 score, accuracy, and precision. Five classifiers including Random Forest, AdaBoost, Gradient Boosting, Logistic Regression, and SVC were used as base classifiers for the ensemble. Ensemble-based optimization (using RSCV) is employed to get the best combination of hyperparameters. This technique performs a randomized search over specified hyperparameter values, using cross-validated performance to find the best combination to create models that make up the ensemble model. Preliminary results indicate promising performance improvements when compared to single model. Future research in this domain could further refine the model and expand its applications in the ever-evolving financial sector.

关键词： Accuracy Static VAr compensators Predictive models Prediction algorithms Reliability engineering Classification algorithms Random forests Standards Optimization Python

来源：评论

学校读者我要写书评

暂无评论

Success Factors of Requirement Elicitation in the Field of software engineering 4

Success Factors of Requirement Elicitation in the Field of S...

引用

4th International Conference on Advancements in computing, ICAC 2022

作者： Attanayaka, Buddhima Nawinna, Dasuni Manathunga, Kalpani Abeygunawardhana, Pradeep K.W. Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Systems & Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398090

Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors. © 2022 IEEE.

关键词： Requirements engineering

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：