检索结果-内蒙古大学图书馆

Statistical data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

Computer Systems science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

来源：评论

学校读者我要写书评

暂无评论

Analysis of Virtualization Based Approach for Detecting Computer Network and its Applications

Analysis of Virtualization Based Approach for Detecting Comp...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Mohamed Abuthahir Riazulhameed, Arshadh Ariff Sreenivas, Nune Usha, S. Rama Lakshmi, Gundala Venkata Subramaniam, Nithyananthi Ramachandran, G. Blackstraw Technologies Pvt Ltd Architect - Data Engineer Tamil Nadu Chennai India Adama Science and Technology University Department of CSE Adama Ethiopia S.A. Engineering College Thiruverkadu Department of MBA Chennai India CVR College of Engineering Department of CSE Hyderabad India Bosch Global Software Technologies Tamilnadu Coimbatore India Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation Deemed to be University ECE Salem India

ISBN: (纸本)9798331540685

The Research presents a virtualization-based method and system for establishing network security services. It includes the following steps: acquiring host devices and computing node sets. based on the host's equipment, determine the link bandwidth;The set of host devices and computing nodes is used to build the computing resource model. The service function chain is implemented using the computing resource model to create network security services. This paper presents a strategy that leverages the features of container-borne network functions for quick deployment, and it suggests several dynamic function deployment techniques. Based on the concept of integer programming optimization problem modeling, the overall problem is broken down into separate sub problems, the optimization problem's scale is reasonably constrained, and quick container deployment is used to guarantee that service request response times are minimal while, to some extent, retaining the performance advantages of simple optimization methods. © 2024 IEEE.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Research on HIV/AIDS Epidemic Trend Prediction Model Based on ARIMA-LSTM 10

Research on HIV/AIDS Epidemic Trend Prediction Model Based o...

引用

10th International Conference on Orange Technology, ICOT 2022

作者： Guo, Xin Yang, Yichen Tseng, Shih-Pang Yin, Zilong Wang, Cheng School of Information Science and Technology Sanda University Shanghai201209 China School of Software and Big Data Changzhou College of Information Technology Changzhou213164 China Shanghai University of Engineering Science Department of Electronic and Electrical Engineering Shanghai201620 China

ISBN: (纸本)9781665454193

In China, the number of persons who have infected Acquired Immune Deficiency Syndrome (AIDS) is rising quickly every year. Various public health and social issues have been brought on by the Human Immunodeficiency Virus/ Acquired Immune Deficiency Syndrome (HIV/AIDS) epidemic. Understanding the epidemic's development trend is crucial for controlling and preventing HIV/AIDS. Long Short-Term Memory (LSTM) is a nonlinear model, whereas Autoregressive Integrated Moving Average model (ARIMA) is a linear model. Both linear and nonlinear tendencies can be seen in China's HIV/AIDS prevalence rates. In order to forecast the HIV/AIDS pandemic trajectory, this research investigates the combined ARIMA-LSTM model. The increasing number of HIV/AIDS patients was predicted using the ARIMA model, the LSTM model, and the ARIMA-LSTM combination model, and their fitting effects were compared. The findings demonstrate that all four models can be used to predict the rising number of HIV/AIDS patients, however the parallel ARIMA-LSTM combination model has a better fitting prediction effect than the other three models. It works better as a forecasting model for the AIDS pandemic trend. © 2022 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

On the noise robustness of in-context learning for text generation 24

On the noise robustness of in-context learning for text gene...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Hongfu Gao Feipeng Zhang Wenyu Jiang Jun Shu Feng Zheng Hongxin Wei Department of Statistics and Data Science Southern University of Science and Technology and School of Economics and Finance Xi'an Jiaotong University School of Economics and Finance Xi'an Jiaotong University Department of Statistics and Data Science Southern University of Science and Technology and National Key Laboratory for Novel Software Technology Nanjing University School of Mathematics and Statistics Xi'an Jiaotong University Department of Computer Science and Engineering Southern University of Science and Technology Department of Statistics and Data Science Southern University of Science and Technology

ISBN: (纸本)9798331314385

Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. Recent works claim that in-context learning is robust to noisy demonstrations in text classification. In this work, we show that, on text generation tasks, noisy annotations significantly hurt the performance of in-context learning. To circumvent the issue, we propose a simple and effective approach called Local Perplexity Ranking (LPR), which replaces the "noisy" candidates with their nearest neighbors that are more likely to be clean. Our method is motivated by analyzing the perplexity deviation caused by noisy labels and decomposing perplexity into inherent perplexity and matching perplexity. Our key idea behind LPR is thus to decouple the matching perplexity by performing the ranking among the neighbors in semantic space. Our approach can prevent the selected demonstrations from including mismatched input-label pairs while preserving the effectiveness of the original selection methods. Extensive experiments demonstrate the effectiveness of LPR, improving the EM score by up to 18.75 on common benchmarks with noisy annotations. Our code is available at https://***/ml-stat-Sustech/Local-Perplexity-Ranking

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Provably Secure and Lightweight Access Control Protocol for EI-Based Vehicle to Grid Environment

引用

IEEE Internet of Things Journal 2023年第18期10卷 16650-16657页

作者： Shamshad, Salman Mahmood, Khalid Shamshad, Usman Hussain, Ibrar Hussain, Shafiq Das, Ashok Kumar The University of Lahore Department of Software Engineering Lahore54590 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan Riphah International University Riphah School of Computing and Innovation Lahore54000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Virginia Modeling Analysis and Simulation Center SuffolkVA23435 United States

The energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional intelligent transportation systems (ITSs) and power grid into a structure that provides assistance to open innovation. The EI offers sustainable and bidirectional transmission for the improvement and analysis of energy convention among electric vehicles (EVs) and service providers. To ensure efficient, reliable, and secure operation, EI must be safe from security attacks. Therefore, efficient and secure key negotiation is a significant issue for the EI-based Vehicle-to-Grid (V2G) architecture. Thus, to ensure the system's security, we have devised a robust scheme to facilitate a secure key agreement between the entities involved for the secure and efficient renewable energy distribution for the EI-based energy vehicles in V2G. The devised scheme's robustness is solicited through the widely accepted formal random or real (RoR) model. In addition, the informal security analysis is conducted on the devised scheme, which is evident that the designed scheme achieves all the required security features of EI-based ITS. Moreover, the performance evaluation results endorse that the designed scheme achieves the desired security and minimizes the communication, computation, and energy overhead by 29.33%, 27.94%, and 28.08% in comparison to the existing competitive schemes. © 2014 IEEE.

关键词： Vehicle-to-grid

来源：评论

学校读者我要写书评

暂无评论

Dual Space Multiple Instance Representative Learning for Medical Image Classification 33

Dual Space Multiple Instance Representative Learning for Med...

引用

33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Zhang, Xiaoxian Huang, Sheng Zhang, Yi Gao, Mingchen Liu, Chen Zhang, Xiaohong School of Big Data & Software Engineering Chongqing University Chongqing China Department of Computer Science and Engineering University at Buffalo SUNY New York United States Department of Radiology First Affiliated Hospital of Army Medical University Chongqing China

Medical image classification plays a vital role in AI-aided medical diagnosis and is often addressed as a Multiple Instance Learning (MIL) issue (i.e., each sample is a bag of instances). For medical images, the disease area or the discriminative area is usually smaller than the whole tissue. In other words, most of the instances in a bag are irrelevant and could interfere with the bag label inference. To address this issue, we add an instance representative selection process before MIL and propose a novel MIL approach named Dual Space Multiple Instance Representative Learning (DSMIRL). DSMIRL consists of two core steps, Adaptive Instance Representative Selection (AIRS) and Multiple Instance Representative Learning (MIRL). In AIRS, the instances in the same bag are grouped into different sub-bags via clustering, and only one sub-bag is selected as the final collection of instance representatives by ranking the maximum instance predictions of sub-bags, thus adaptively filtering out the irrelevant instances. In MIRL, we perform aggregations on the selected instance representatives in label and feature spaces to further exploit the complementary information of the two spaces. Finally, these two steps are iteratively conducted in each iteration to optimize all modules of DSMIRL progressively. Extensive experiments on five standard MIL benchmarks and two medical image datasets demonstrate the promising performance of DSMIRL over the state-of-the-art MIL approaches. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs? 23

What Makes Good In-Context Demonstrations for Code Intellige...

引用

Proceedings of the 38th IEEE/ACM International Conference on Automated software engineering

作者： Shuzheng Gao Xin-Cheng Wen Cuiyun Gao Wenxuan Wang Hongyu Zhang Michael R. Lyu School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong China School of Big Data and Software Engineering Chongqing University China

ISBN: (纸本)9798350329964

Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models have shown the ability of in-context learning (ICL). ICL employs task instructions and a few examples as demonstrations, and then inputs the demonstrations to the language models for making predictions. This new learning paradigm is training-free and has shown impressive performance in various natural language processing and code intelligence tasks. However, the performance of ICL heavily relies on the quality of demonstrations, e.g., the selected examples. It is important to systematically investigate how to construct a good demonstration for code-related tasks. In this paper, we empirically explore the impact of three key factors on the performance of ICL in code intelligence tasks: the selection, order, and number of demonstration examples. We conduct extensive experiments on three code intelligence tasks including code summarization, bug fixing, and program synthesis. Our experimental results demonstrate that all the above three factors dramatically impact the performance of ICL in code intelligence tasks. Additionally, we summarize our findings and provide takeaway suggestions on how to construct effective demonstrations, taking into account these three perspectives. We also show that a carefully-designed demonstration based on our findings can lead to substantial improvements over widely-used demonstration construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%, 175.96%, and 50.81% on code summarization, bug fixing, and program synthesis, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced Synthetic Images Classification with Vision Transformers and Effective data Augmentation Techniques

Enhanced Synthetic Images Classification with Vision Transfo...

引用

IEEE International Conference on Multi Topic

作者： Khadija Haider Muhammad Sohail Abbas Asif Muhammad Muhid Qaiser Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Software Engineering National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Synthetic images have made significant advancements, resulting in highly realistic and high-quality visuals generated at an unprecedented speed. However, the proliferation of synthetic images raises growing concerns regarding their potential misuse, ethical implications, and associated risks in various sectors. This study focuses on leveraging data augmentation techniques to enhance the accuracy and reliability of synthetic image detection. In our study, we fine-tuned a Vision Transformer (ViT) on the CIFAKE dataset, comprising real and synthetic images, augmented using various techniques such as transformations, photometric adjustments, geometric changes, and noise addition. The best results were obtained with ViT fine-tuned by distortions and noise augmentation, achieving an accuracy rate of 98.66%, demonstrating that sophisticated augmentation techniques improve performance. data augmentation plays a crucial role in handling the increasing realism and sophistication of synthetic images, contributing to more effective content moderation, digital forensics efforts, and the mitigation of potential risks associated with synthetic media.

关键词： Performance evaluation Computer vision Visualization Accuracy Computational modeling Transformers Distortion data augmentation Reliability Image classification

来源：评论

学校读者我要写书评

暂无评论

Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting

arXiv

引用

arXiv 2024年

作者： Ruan, Weilin Wang, Wenzhuo Zhong, Siru Chen, Wei Liu, Li Liang, Yuxuan The Hong Kong University of Science and Technology Guangzhou China The Computer Science Department Jinan University Guangzhou China The School of Big Data and Software Engineering Chongqing University Chongqing China

Predicting spatio-temporal traffic flow presents significant challenges due to complex interactions between spatial and temporal factors. Existing approaches often address these dimensions in isolation, neglecting their critical interdependencies. In this paper, we introduce the Spatio-Temporal Unitized Model (STUM), a unified framework designed to capture both spatial and temporal dependencies while addressing spatio-temporal heterogeneity through techniques such as distribution alignment and feature fusion. It also ensures both predictive accuracy and computational efficiency. Central to STUM is the Adaptive Spatio-temporal Unitized Cell (ASTUC), which utilizes low-rank matrices to seamlessly store, update, and interact with space, time, as well as their correlations. Our framework is also modular, allowing it to integrate with various spatio-temporal graph neural networks through components such as backbone models, feature extractors, residual fusion blocks, and predictive modules to collectively enhance forecasting outcomes. Experimental results across multiple real-world datasets demonstrate that STUM consistently improves prediction performance with minimal computational cost. These findings are further supported by hyperparameter optimization, pre-training analysis, and result visualization. We provide our source code for reproducibility at https://***/r/STUM-E4F0. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A Novel Machine Learning Based Framework for Bridge Condition Analysis

A Novel Machine Learning Based Framework for Bridge Conditio...

引用

2022 IEEE International Conference on Big data, Big data 2022

作者： Masum, Mohammad Anjum, Nafisa Hossain Faruk, Md Jobair Shahriar, Hossain Sakib, Nazmus Valero, Maria Karim, M.A. Rahman, Akond Wu, Fan Cuzzocrea, Alfredo San Jose State University Department of Applied Data Science San Jose United States Kennesaw State University Department of Information Technology United States Kennesaw State University Department of Software Engineering and Game Development United States Kennesaw State University Department of Civil and Environmental Engineering United States Auburn University Department of Computer Science and Software Engineering United States Tuskegee University Department of Computer Science United States University of Calabria IDEA Lab Rende Italy

ISBN: (纸本)9781665480451

Bridges play a vital part in the transportation system by ensuring the connectedness of transportation systems, which is critical for a country's social and economic prosperity by offering daily mobility to the people. However, according to the American Society of Civil Engineers (ASCE 2017), many U.S. bridges are in critical condition, raising safety issues, with 9.1 and 13.6 percent of the country's 614,387 bridges, respectively, structurally defective, and functionally obsolete. Every day, 178 million people traverse these structurally defective bridges. Furthermore, the average annual failure rate is expected to be between 87 and 222. Bridge breakdowns have disastrous repercussions, and in many cases, result in death. While bridge authorities strive to improve bridge conditions, budget limits make it difficult to make cost-effective maintenance decisions. Bridge authorities distribute limited repair resources based on projected future bridge conditions. As a result, building a data-driven, autonomous, and effective bridge condition prediction model is critical for improving maintenance decision-making. In this paper, we present a novel bridge condition prediction framework using advanced Machine Learning (ML) algorithms on the National Bridge Inventory (NBI) dataset. The framework consists of two stages, where the most informative features from the NBI dataset are selected using the Recursive Feature Elimination process and in the 2nd step, ML classifiers are applied to the selected features for bridge condition prediction. The experimental results show that the proposed framework can effectively predict bridge conditions by producing highly accurate results in terms of accuracy, precision, recall, and f1-score. © 2022 IEEE.

关键词： Forecasting

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：