检索结果-内蒙古大学图书馆

2023 International Conference on Energy, Power, Environment, control, and Computing, ICEPECC 2023

作者： Asim, Sherrjel Nayyer, Muhammad Ziad Ahmed, Usama Faheem, Muhammad GIFT University Department of Computer Science Gujranwala Pakistan Government College University Department of Software Engineering Faisalabad Pakistan

ISBN: (纸本)9781665476010

Rapidly increasing data requires a lot of time to execute different tasks by the systems at Cloudlet Federation (CF). The demand of today's computing is to exploit resource utilization up to maximum. Efficient resource allocation and scheduling of tasks are the significant characteristics of Cloudlet Federation (CF). Different tasks have diverse execution time. Some tasks require greater execution time whereas some need just a couple of seconds for their execution. Scheduling offers a feasible solution for handling these tasks with the help of different scheduling algorithms. To address task scheduling issue, this paper proposes a scheduling technique named Least Busy Time Scheduling (LBTS) for smart scheduling of tasks in Cloudlet Federation (CF). The proposed technique assists the scheduler to assign task to a system considering the parameter of least busy time to reduce waiting time and optimize the overall performance of the system. © 2023 IEEE.

关键词： Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

引用

Intelligent Automation & Soft Computing 2023年第8期37卷 2247-2261页

作者： Amani Yahyaoui Jawad Rasheed Shtwai Alsubai Raed M.Shubair Abdullah Alqahtani Buket Isler Rana Zeeshan Haider Department of Software Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Nisantasi UniversityIstanbul34398Turkey Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Electrical and Computer Engineering New York University(NYU)Abu Dhabi129188United Arab Emirates College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Engineering Istanbul Topkapi UniversityIstanbul34087Turkey Baqai Institute of Hematology Baqai Medical UniversityKarachi75340Pakistan

The coronavirus(COVID-19)is a disease declared a global pan-demic that threatens the whole *** then,research has accelerated and varied to find practical solutions for the early detection and correct identification of this *** researchers have focused on using the potential of Artificial Intelligence(AI)techniques in disease diagnosis to diagnose and detect the *** paper developed deep learning(DL)and machine learning(ML)-based models using laboratory findings to diagnose *** different methods are used in this study:K-nearest neighbor(KNN),Decision Tree(DT)and Naive Bayes(NB)as a machine learning method,and Deep Neural Network(DNN),Convolutional Neural Network(CNN),and Long-term memory(LSTM)as DL *** approaches are evaluated using a dataset obtained from the Israelita Albert Einstein Hospital in Sao Paulo,*** data consists of 5644 laboratory results from different patients,with 10%being Covid-19 positive *** dataset includes 18 attributes that characterize *** used accuracy,f1-score,recall and precision to evaluate the different developed *** obtained results confirmed these approaches’effectiveness in identifying COVID-19,However,ML-based classifiers couldn’t perform up to the standards achieved by DL-based *** all,NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56%and 85%accuracies,*** these,DL models attained better performance as CNN,DNN and LSTM secured more than 90%*** LTSM outperformed all by achieving an accuracy of 96.78%and an F1-score of 96.58%.

关键词： Artificial intelligence COVID-19 deep learning diagnosis machine learning

来源：评论

学校读者我要写书评

暂无评论

Low-confidence Heterogeneous Pixel-Prototype Contrastive Learning for Semi-supervised Medical Image Segmentation

Low-confidence Heterogeneous Pixel-Prototype Contrastive Lea...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, Shihuan Lai, Zhihui Kong, Heng Shenzhen University College of Computer Science and Software Engineering Shenzhen China Baoan Central Hospital of Shenzhen Department of Breast and Thyroid Surgery Shenzhen China

ISBN: (纸本)9798350386226

Medical image segmentation is a challenging task especially when dealing with unlabeled data. It has been proven that the use of complementary information for co-training is effective for medical image segmentation. We find that high-confidence regions are easy to segment, but low-confidence regions are difficult to correctly segment, which results in poor segmentation performance. Therefore, accurate segmentation in low-confidence regions is the effective way to enhance the performance of medical image segmentation. To achieve this goal, we propose a novel co-training strategy named low-confidence heterogeneous pixel-prototype contrastive learning with uncertainty-guide cross supervision for semi-supervised medical image segmentation. Specifically, an uncertainty-guided cross supervision module is firstly designed to estimate the confidence score of predictions from multiple outputs and then perform cross supervision between high-confidence regions. Moreover, we design a low-confidence heterogeneous prototype contrastive learning, which builds the relationship between low-confidence pixel and heterogeneous prototype to mine discriminative information in low-confidence regions so as to improve the segmentation performance. Extensive experiments on medical image datasets demonstrate that our method outperforms state-of-the-art methods in segmentation results. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Trajectory Tracking control of Autonomous Vehicles based on Improved Model-free Adaptive control 3

Trajectory Tracking Control of Autonomous Vehicles based on ...

引用

3rd International Conference on Testing Technology and Automation engineering, TTAE 2023

作者： Wang, Wanting Wang, Siming Han, Taoguo School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou730070 China College of Computer and control engineering Northeast Forestry University Harbin150000 China

ISBN: (纸本)9781510674769

This paper presents a novel approach to address the lateral control issue in trajectory tracking for autonomous cars. Traditional model-free adaptive control algorithms have some limitations, prompting the development of an enhanced data-driven model-free adaptive control scheme. More precisely, inside a specified trajectory, the vehicle's issue of lateral control is converted into a problem of tracking the preview-deviation-angle. A dynamic linearized data model is developed for the lateral control of autonomous cars, taking into account the input and output data. To enhance the steering angle control rate, a time-varying proportional control term is incorporated. An enhanced trajectory tracking control technique for the MFAC system is proposed, followed by a demonstration of its convergence. The simulation findings demonstrate that the IMFAC scheme exhibits a superior output response compared to both MFAC and PID in both low-speed and high-speed scenarios. Furthermore, the IMFAC system is capable of achieving autonomous vehicle path tracking. © 2024 SPIE.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Resource Availability Forecasting for Federated Clouds

Resource Availability Forecasting for Federated Clouds

引用

2023 International Conference on Energy, Power, Environment, control, and Computing, ICEPECC 2023

作者： Mursal, Muhammad Hassan Ahmed, Usama Nayyer, Muhammad Ziad Government College University Department of Software Engineering Faisalabad Pakistan GIFT University Department of Computer Science Gujranwala Pakistan

ISBN: (纸本)9781665476010

Cloud federation has enabled organizations to adopt collaborative services for sharing data and workloads across various platforms. Induction of federation members may require some verifications and predictions related to capacity and capability of these members for compensating such types of workloads. However, the nature of federated services require stringent methods to keep track of dynamically forming resource clusters for forecasting their behavior. Recent literature has mostly focused on the applicability of forecasting algorithms based on static datasets with little or no applicability to real time scenarios. Proposed research has utilized a real world application of Clouds4Coordination (C4C) federation system. A resource forecasting strategy using two well-known algorithms, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) has been proposed for collaborative clouds used in Architecture, engineering and Construction (AEC) industry. The results have shown that no single algorithm is sufficient enough to deal with dynamic scenarios of cloud federation. Moreover, the selection of algorithm is highly dependent upon the type and duration of prediction required i.e. short term or long term as required by the user. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Classification of Unbalanced Data Based on Bayesian Optimal Neural Network Model

Journal of Network Intelligence

引用

Journal of Network Intelligence 2024年第3期9卷 1427-1440页

作者： Ye, Li-Zhu Alkawaz, Mohammed Hazim Johar, Md Gapar Md School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou511363 China School of Graduate Studies Management and Science University 40100 Shah Alam Selangor Malaysia Department of Computer Science College of Education for Pure Science University of Mosul Mosul41002 Iraq Software Engineering and Digital Innovation Center Management and Science University Shah Alam40100 Malaysia

Aiming at the unbalanced problem of big data classification, this paper proposes a classification method of unbalanced data on the ground of Bayesian optimal neural network model. The method firstly uses Optimized Graph Convolutional Network (OGCN) network to carry out message passing mechanism in data graph, designs neural network model with self-attention pooling and mean maximization pooling, and then uses Bayes optimization to update the posterior probability of optimization function by constantly updating the probability model and evaluating the objective function. Effectively solve the problem of speed and optimal solution of hyperparameter optimization in the neural network model. Secondly, the model is used for data set training to obtain the weight of the entire network. Secondly, the unbalanced data is mixed sampling to reduce the number of negative samples. Finally, the capability of the model was verified on the CiteSeer dataset. Experimental results showed that the accuracy rate, accuracy rate and recall rate of the proposed algorithm were 0.974, 0.982 and 0.973, respectively, which effectively improved the accuracy rate, accuracy rate and recall rate of unbalanced data classification compared with other algorithms. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

A Survey on the Emerging Trend of Human Microchip Implants And Their Potential

A Survey on the Emerging Trend of Human Microchip Implants A...

引用

2023 Advances in Science and engineering Technology International Conferences, ASET 2023

作者： Al Mansouri, Ltifa Mohammed Khalifa Alnajjar, Fady College of It United Arab Emirates University Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781665454742

Human microchip implants are small devices that are implanted into the body for various purposes including storing and transmitting information, monitoring health or location, and providing access to facilities or systems. Although human microchip implants offer numerous advantages, their adoption remains low in the public sector. This article presents a comprehensive survey on the emerging trend of human microchip implants and their potential uses in various fields. In addition to introducing the general concept of microchip implants, the work discusses their current and potential future applications. To better understand the direction of this trend, recent case studies of existing applications of human microchip implants are analyzed and presented. The article also explores the barriers to the adoption of human microchip implants and discusses their potential impact on humans, whether positive or negative. © 2023 IEEE.

关键词： Near field communication

来源：评论

学校读者我要写书评

暂无评论

Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation 28th

Self-Sampling Training and Evaluation for the Accuracy-Bi...

引用

28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Liu, Dugang Qiao, Yang Tang, Xing Chen, Liang He, Xiuqiang Pan, Weike Ming, Zhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China FIT Tencent Shenzhen China

ISBN: (纸本)9783031306778

Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most of the existing methods require some specific data, architectures and training methods. In this paper, we first argue through an online study that arbitrarily removing all the biases in industrial recommendation may not consistently yield a desired performance improvement. For the situation that a randomized dataset is not available, we propose a novel self-sampling training and evaluation (SSTE) framework to achieve the accuracy-bias tradeoff in recommendation, i.e., eliminate the harmful biases and preserve the beneficial ones. Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data. A self-training module infers the beneficial biases and learns better tradeoff based on these subsets, and a self-evaluation module aims to use these subsets to construct more plausible references to reflect the optimized model. Finally, we conduct extensive offline experiments on two datasets to verify the effectiveness of our SSTE. Moreover, we deploy our SSTE in homepage recommendation of a famous financial management product called Tencent Licaitong, and find very promising results in an online A/B test. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Enhanced Metaheuristics-Based Clustering Scheme for Wireless Multimedia Sensor Networks

引用

computers, Materials & Continua 2022年第11期73卷 4179-4192页

作者： R.Uma Mageswari Sara A.Althubiti Fayadh Alenezi E.Laxmi Lydia Gyanendra Prasad Joshi Woong Cho Department of Computer Science and Engineering Vardhaman College of Engineering(Autonomous)HyderabadTelangana501218India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

Traditional Wireless Sensor Networks(WSNs)comprise of costeffective sensors that can send physical parameters of the target environment to an intended *** the evolution of technology,multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target *** existence of multimedia sensors,integrated with effective signal processing and multimedia source coding approaches,has led to the increased application of Wireless Multimedia Sensor Network(WMSN).This sort of network has the potential to capture,transmit,and receive multimedia *** energy is a major source in WMSN,novel clustering approaches are essential to deal with adaptive topologies of WMSN and prolonged network *** this motivation,the current study develops an Enhanced Spider Monkey Optimization-based Energy-Aware Clustering Scheme(ESMO-EACS)for *** proposed ESMO-EACS model derives ESMO algorithm by incorporating the concepts of SMO algorithm and quantum *** proposed ESMO-EACS model involves the design of fitness functions using distinct input parameters for effective construction of clusters.A comprehensive experimental analysis was conducted to validate the effectiveness of the proposed ESMO-EACS technique in terms of different performance *** simulation outcome established the superiority of the proposed ESMO-EACS technique to other methods under various measures.

关键词： Wireless multimedia sensor networks clustering spider monkey optimization algorithm energy efficiency metaheuristics quantum computing

来源：评论

学校读者我要写书评

暂无评论

An Improved Fault-Tolerant control and Stabilization for Positive Polynomial Fuzzy Systems

An Improved Fault-Tolerant Control and Stabilization for Pos...

引用

2024 IEEE International Conference on control Science and Systems engineering, ICCSSE 2024

作者： Wang, Ziguang Meng, Aiwen Wang, Mingxuan Li, Zhiquan Yanshan University Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei China College of Electronic and Information Engineering Hebei University Hebei China

ISBN: (纸本)9798331517199

This paper presents an improved analysis method to design a fault-tolerant controller for positive polynomial fuzzy systems based on static output feedback as well as actuator faults. Firstly, inspired by the property of fault matrix, a new approach of controller design is presented, and some constraint conditions are introduced to guarantee the stability conditions and posi-tivity conditions to be convex simultaneously. Furthermore, by comparing with the existing methods, this method not only can eliminate the binding to the input matrix B, but also is able to provide a wider range of stability region. Besides, to enhance the relaxation of the stability analysis, we introduce the up-to-date Taylor series membership functions (TSMFs) approximation method such that the information embedded in the membership functions and the state boundary information can be taken into the stability conditions. As a result, on the strength of Lyapunov stability theory, the stability analysis is performed via sum-of-squares (SOS) approach. Finally, a simulation example is given to illustrate the effectiveness and reliability of the proposed method. © 2024 IEEE.

关键词： Fuzzy systems

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：