检索结果-内蒙古大学图书馆

Advancements in Computing (ICAC), International Conference on

作者： Tharindu Gunasekara Disira Thihan Sansika Kodithuwakku Dinuka Dissanayake Harinda Fernando Samitha Vidhanaarachchi Archchana Sindhujan dept. of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka dept. of Computer Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka dept. of Computer Science and Electronic Engineering University of Surrey United Kingdom

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods.

关键词： Sentiment analysis Analytical models Accuracy Bidirectional control Logic gates Linguistics Transformers Encoding Vectors Fake news

来源：评论

学校读者我要写书评

暂无评论

Scavenger: Better Space-Time Trade-Offs for Key-Value Separated LSM-trees 40

Scavenger: Better Space-Time Trade-Offs for Key-Value Separa...

引用

40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Jianshun Wang, Fang Qiu, Sheng Wang, Yi Ou, Jiaxin Huang, Junxun Li, Baoquan Fang, Peng Feng, Dan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China China Shenzhen Huazhong University of Science and Technology Research Institute China Bytefrance Inc.

ISBN: (纸本)9798350317152

Key-Value Stores (KVS) implemented with log-structured merge-tree (LSM-tree) have gained widespread ac-ceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space am-plification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV-separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB). © 2024 IEEE.

关键词： Economic and social effects

来源：评论

学校读者我要写书评

暂无评论

Improved Ingredients-based Recipe Recommendation software using Machine Learning

Improved Ingredients-based Recipe Recommendation Software us...

引用

IEEE International Conference on Intelligent Computing and information systems (ICICIS)

作者： Mariam Ashraf Khadija Tarek Naglaa Fathy Mariam Mahmoud Rana Emad Khadija Khaled Noran Amr Software Engineering Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Information Systems Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Smart Home User Interface: Development and Comparison

Smart Home User Interface: Development and Comparison

引用

International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： R. Faizrakhmanov A. Platunov M. Bahrami Cyberphysical Systems Laboratory Innopolis University Kazan Russian Faculty of Software Engineering and Computer Systems ITMO University Saint-Petesburg Russia

The active development of the Internet of Things (IoT) in recent years has increased people’s need for control smart devices for home. At the same time, the complexity of these devices is growing. Therefore, Smart Home systems require a simple and convenient interface for control. However, methods for evaluating and comparing interfaces are not clear. In this paper, we described a method to comparing and evaluating different types of Smart Home interfaces. In addition, we evaluate results experimentally. For this comparison, we used a popular Smart Home control software on the market. The results showed that Smart Home graphical user interfaces are more convenient than a messenger or voice. Voice interface is suitable for controlling a Smart Home but is not informative enough in feedback. On the other hand, the familiar interface of the messenger turned out to be the least convenient. This study will help both researchers and practitioners evaluate and choose the right Smart Home interface.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automated Classification of Medical Images Using Convolutional Neural Networks 15

Automated Classification of Medical Images Using Convolution...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pillai, N. Muthuvairavan Manimala, S. Rongali, Aruna Sri Gugnani, Anchit Kumar, Senthil A. Sriramakrishnan, G.V. Computer Science and Business Systems R.M.D Engineering College Thiruvallur district Kavarapettai India Computer science and engineering University SJCE JSS Science and technology University Karnataka Mysore India Sr. Software Engineer Department Information Technology Emphasis/Information Technology Ph.D University The country University of The Cumberlands United States Physiotherapy Amity University Rajasthan Jaipur India Artificial Intelligent department Shri Vishnu Engineering College for Women AP Bhimavaram India Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort. ©2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

引用

International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC)

作者： Ilayaraja V S.P. Rajamohana Jagadeesan S V. Aishwarya M. Rakesh School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science School of Engineering & Technology Pondicherry University Karaikal Puducherry India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability.

关键词： Deep learning Surveys Accuracy Tropical cyclones Disasters Sea measurements Estimation Transformers Satellite images Reliability

来源：评论

学校读者我要写书评

暂无评论

Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines

arXiv

引用

arXiv 2024年

作者： Long, Kehan Li, Shasha Wang, Pancheng Bao, Chenlong Tang, Jintao Wang, Ting School of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China

Citing comprehensively and appropriately has become a challenging task with the explosive growth of scientific publications. Current citation recommendation systems aim to recommend a list of scientific papers for a given text context or a draft paper. However, none of the existing work focuses on already included citations of full papers, which are imperfect and still have much room for improvement. In the scenario of peer reviewing, it is a common phenomenon that submissions are identified as missing vital citations by reviewers. This may lead to a negative impact on the credibility and validity of the research presented. To help improve citations of full papers, we first define a novel task of Recommending Missed Citations Identified by Reviewers (RMC) and construct a corresponding expert-labeled dataset called CitationR. We conduct an extensive evaluation of several state-of-the-art methods on CitationR. Furthermore, we propose a new framework RMCNet with an Attentive Reference Encoder module mining the relevance between papers, already-made citations, and missed citations. Empirical results prove that RMC is challenging, with the proposed architecture outperforming previous methods in all metrics. We release our dataset and benchmark models to motivate future research on this challenging new task. Copyright © 2024, The Authors. All rights reserved.

关键词： Paper

来源：评论

学校读者我要写书评

暂无评论

Smart Home User Interface: Overview

Smart Home User Interface: Overview

引用

International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： Renat Faizrakhmanov Alexey Platunov Mohammad Reza Bahrami Cyberphysical Systems Laboratory Innopolis University Kazan Russian Faculty of Software Engineering and Computer Systems ITMO University Saint-Petesburg Russia

This paper overviews the Smart Home User Interfaces (UI) in the following aspects: general definition of Human-Machine Interface (HMI), types of common Smart Home UI, market analysis of HMI, voice assistants and chatbots. This study was conducted to determine existed Smart Home UI and evaluate them in the future works. This comparing and evaluation needed to choose appropriate and convenient Smart Home UI for certain application. The overview leaded to the following findings: Smart Home market is growing; certain global trends influenced on this market and on HMI in general; certain groups of people are more interested in Smart Home technologies; each type of Smart Home User Interface has its leaders. This study can help researchers learn about the main types of Smart Home User Interfaces and trends in this market.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Migrating Birds Optimization-Based Feature Selection for Text Classification

arXiv

引用

arXiv 2024年

作者： Kaya, Cem Kilimci, Zeynep Hilal Uysal, Mitat Kaya, Murat Dogus University Turkey Department of Information Systems Engineering Kocaeli University Turkey Department of Software Engineering Dogus University Turkey Department of Computer Programming Acibadem University Turkey

This research introduces a novel approach, MBO-NB, that leverages Migrating Birds Optimization (MBO) coupled with Naive Bayes as an internal classifier to address feature selection challenges in text classification having large number of features. Focusing on computational efficiency, we preprocess raw data using the information Gain algorithm, strategically reducing the feature count from an average of 62221 to 2089. Our experiments demonstrate MBO-NB's superior effectiveness in feature reduction compared to other existing techniques, emphasizing an increased classification accuracy. The successful integration of Naive Bayes within MBO presents a well-rounded solution. In individual comparisons with Particle Swarm Optimization (PSO), MBO-NB consistently outperforms by an average of 6.9% across four setups. This research offers valuable insights into enhancing feature selection methods, providing a scalable and effective solution for text classification. © 2024, CC BY-NC-ND.

关键词： Feature Selection

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：