检索结果-内蒙古大学图书馆

7th EAI International Conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2024

作者： Arinaitwe, Irene Jonathan, Ssemakula Nakakawa, Agnes Maiga, Gilbert School of Computing and Informatics Technology Makerere University Kampala Uganda

ISBN: (纸本)9783031864926

Effective waste management relies on active public participation, yet engaging citizens in sustainable waste management remains a challenge. This study introduces a novel approach by developing a mobile application to support public involvement in waste management processes. Drawing upon the Persuasive Systems Design (PSD) framework, the application incorporates persuasive strategies to motivate users to adopt responsible waste handling behaviors. The developed mobile application features a set of functionalities aimed at facilitating user engagement and behavior change. These include a leaderboard to foster competition and motivation among users, a chat room for community interaction, a sorting guide for proper waste categorization, and a module for locating nearby waste collectors, thereby promoting accessibility. To assess the effectiveness and user satisfaction of the application, a structured walkthrough evaluation method was employed, complemented by questionnaire items based on the Attention, Relevance, Confidence, and Satisfaction (ARCS) model. Through this evaluation process, the study measured users’ attention to the application’s features, the relevance of its content to their needs, their confidence in using the application, and their overall satisfaction with the experience. The findings of the evaluation demonstrate the potential of the mobile application in effectively engaging users and promoting positive changes in waste management behaviors. © ICST Institute for Computer Sciences, Social informatics and Telecommunications Engineering 2025.

关键词： Municipal solid waste

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

引用

Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

来源：评论

学校读者我要写书评

暂无评论

Sinhala Character Identification Using Orientation and Support Vector Machine 4

Sinhala Character Identification Using Orientation and Suppo...

引用

4th International Conference on Advanced Research in computing, ICARC 2024

作者： Thamarasee, K.D. Surendra, R.M.D.B. School of Computing Informatics Institute of Technology Colombo Sri Lanka School of Computing Universal College Lanka Colombo Sri Lanka

ISBN: (纸本)9798350384864

In this paper we proposed a method for printed Sinhala character recognition based on support vector classification and histogram oriented gradient descriptor (HOG descriptor). Converting the text on images into text is a challenging task. It becomes the most difficult task when considering local languages. Having 400+ characters in the Sinhala alphabet and the curve pattern of the letters causes some difficulties when converting the Sinhala text images into text. A histogram oriented gradient descriptor is used to extract the unique features of each individual character. A linear support vector machine is used to classify each character. Sinhala corpus was created to check the word correctness and permutation method used for the process of creating words. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Improving Quality and Domain-Relevancy of Paraphrase Generation with Graph-Based Retrieval Augmented Generation 24

Improving Quality and Domain-Relevancy of Paraphrase Generat...

引用

10th International Conference on computing and Artificial Intelligence, ICCAI 2024

作者： Jayawardena, Lasal Yapa, Prasan School of Computing Robert Gordon University Aberdeen United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (纸本)9798400717055

Paraphrase generation is a fundamental area of research in Natural Language Processing (NLP) and Natural Language Generation (NLG), due to its sequence-to-sequence (Seq2Seq) nature. Paraphrasing, spanning across various domains, poses challenges for simpler model architectures due to the extensive knowledge required to generate paraphrases. The added constraint of generating diverse paraphrases further complicates the task for models trained on existing datasets. We present a methodology that leverages Graph-Based Retrieval Augmented Generation (G-RAG), capable of utilizing both entity and phrasal knowledge to address this issue. We demonstrate through experiments that this approach enables both complex models like Large Language models (LLMs) and smaller Seq2Seq models to generate more diverse paraphrases without compromising semantic similarity. Furthermore, this approach's capacity to integrate domain-specific knowledge makes it particularly effective across different domains, enhancing its applicability in varied contexts. The results are further corroborated by human evaluation and extensive quantitative analysis focusing on semantic similarity, lexical diversity, syntactic diversity, and grammatical correctness to gauge high-quality paraphrases. © 2024 ACM.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Improved Hungarian algorithm-based task scheduling optimization strategy for remote sensing big data processing

引用

地球空间信息科学学报（英文版） 2024年第4期27卷 1141-1154页

作者： Sheng Zhang Yong Xue Heng Zhang Xiran Zhou Kaiyuan Li Runze Liu School of Environment and Spatial Informatics China University of Mining and TechnologyXuzhouChina School of Environment and Spatial Informatics China University of Mining and TechnologyXuzhouChina School of Computing and Engineering University of DerbyDerbyUK Artificial Intelligence Research Institute China University of Mining and TechnologXuzhouChina Shandong Province Institute of Land Surveying and Mapping JinanChina School of Environment and Spatial Informatics China University of Mining and TechnologyXuzhouChina Artificial Intelligence Research Institute China University of Mining and TechnologXuzhouChina

With the development of remote sensing technology and computing science,remote sensing data present typical big data *** rapid development of remote sensing big data has brought a large number of data processing tasks,which bring huge challenges to *** computing is the primary means to process remote sensing big data,and task scheduling plays a key role in this *** study analyzes the characteristics of batch processing of remote sensing big *** paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow,called optimal sequence dynamic assignment algorithm,which is applicable to hetero-geneously distributed computing *** strategy has two core contents:the improved Hungarian algorithm model and the multi-level optimal assignment task queue ***,the strategy solves the dependency,mismatch,and computational resource idleness problems in the optimal scheduling of remote sensing batch processing *** proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational *** experimented with the aerosol optical depth retrieval algorithm workflow using this *** with the processing before optimization,the makespan of the proposed method was shortened by at least 20％.Compared with popular scheduling algorithm,the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.

关键词： Workflow Hungarian algorithm optimal assignment remote sensing big data large-scale task

来源：评论

学校读者我要写书评

暂无评论

Rice Leaf Disease Detection Using Deep Learning Algorithm

Mari Papel y Corrugado

引用

Mari Papel y Corrugado 2024年第1期2024卷 165-172页

作者： Abdullahi, Akibu Mahmoud Hassan, Isyaku Hossain, Md Shakil Abubakar, Yusuf School of Computing and Information Technology Taylor's University Selangor Subang Jaya47500 Malaysia Faculty of Languages and Communication Universiti Sultan Zainal Abidin Terengganu Malaysia School of Computing and Informatics Albukhary International University Alor Setar Malaysia

Rice cultivation faces significant challenges due to diseases such as bacterial blight, Blast, brown spot, and tungro, which diminish the production and quality of crops. Additionally, traditional methods of detecting these diseases are difficult and prone to mistakes. This situation worsens production losses and risks food security. This research seeks to overcome this issue by examining the effectiveness of deep learning techniques, particularly Convolutional Neural Networks (CNNs), in automating the detection of rice leaf disease. We collected a diverse data set consisting of 6229 pre-processed photos of common rice leaf diseases and healthy images. This dataset was used to train the CNN model, and performance was assessed using evaluation measures including confusion matrices and accuracy assessment. The results showed significant performance improvements, with the CNN model obtaining a final validation accuracy of 98.98%. In detecting rice leaf diseases, our method exceeded other deep learning systems. In places where rice production is the primary source of food security, this work highlights the potential of CNN-based models in automated rice disease detection, providing an effective way to improve crop productivity. © The authors.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

引用

Frontiers of Computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based Framework for Reliable Sri Lankan Currency Authentication and Counterfeit Prevention 13

Deep Learning Based Framework for Reliable Sri Lankan Curren...

引用

13th IEEE International Conference on System Engineering and technology, ICSET 2023

作者： Abhishek, Yohan Nadeem, Nishaam Delankawala, Sayumi Jayakody, Senesh Sumanathilaka, T.G.D.K. Informatics Institute of Technology School of Computing Colombo006 Sri Lanka

ISBN: (纸本)9798350340891

Digital image processing plays a crucial role in enabling efficient and precise analysis, manipulation, and enhancement of images. In this study, researchers address challenges faced by individuals with visual impairments in recognizing currency denominations and identifying counterfeit banknotes. The researchers propose 'Blind Trust,' an IoT device that utilizes an Arduino Uno and a camera module to capture images of banknotes. To achieve these objectives, researchers utilize pre-processing techniques using the OpenCV and TensorFlow libraries to extract the notes/coins' characteristics. Custom datasets are developed for training Convolutional Neural Network (CNN) models, which are then used to identify currency denominations and detect counterfeit currency. To enhance the model's performance, various preprocessing techniques are employed, resulting in high accuracy rates for both tasks. The currency notes identification model achieves an impressive 99% accuracy when tested on 25% of the data, while the currency coins identification model achieves 93% accuracy using InceptionV3. Additionally, the counterfeit currency detection model, created using VGG16, achieves an accuracy rate of 97% on a dataset comprising genuine and counterfeit currency images. Moreover, the note placement detection model attains 93% accuracy. 'Blind Trust' holds great potential for enhancing financial security and accessibility for individuals with visual impairments. Its accuracy, speed, and ease of use contribute significantly to the development of new technologies aimed at improving their quality of life. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

FilterGNN:Image feature matching with cascaded outlier filters and linearattention

引用

Computational Visual Media 2024年第5期10卷 873-884页

作者： Jun-Xiong Cai Tai-Jiang Mu Yu-Kun Lai Key Laboratory of Pervasive Computing Ministry of EducationDepartment of Computer Science and TechnologyTsinghua UniversityBeijing 100084China School of Computer Science and Informatics Cardiff UniversityWales CF244AGUK

The cross-view matching of local image features is a fundamental task in visual localization and 3D *** study proposes FilterGNN,a transformer-based graph neural network(GNN),aiming to improve the matching efficiency and accuracy of visual *** on high matching sparseness and coarse-to-fine covisible area detection,FilterGNN utilizes cascaded optimal graph-matching filter modules to dynamically reject outlier ***,we successfully adapted linear attention in FilterGNN with post-instance normalization support,which significantly reduces the complexity of complete graph learning from O(N2)to O(N).Experiments show that FilterGNN requires only 6%of the time cost and 33.3%of the memory cost compared with SuperGlue under a large-scale input size and achieves a competitive performance in various tasks,such as pose estimation,visual localization,and sparse 3D reconstruction.

关键词： image matching transformer linear attention visual localization sparse reconstruction

来源：评论

学校读者我要写书评

暂无评论

Explainable Transformer-based Intrusion Detection in Internet of Medical Things (IoMT) Networks 23

Explainable Transformer-based Intrusion Detection in Interne...

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Kalakoti, Rajesh Nomm, Sven Bahsi, Hayretdin School of Information Technology TalTech Tallinn Estonia School of Informatics Computing and Cyber Systems Northern Arizona University United States

ISBN: (纸本)9798350374889

Internet of Medical Things (IoMT) systems have brought transformative benefits to patient monitoring and remote diagnosis in healthcare. However, these systems are prone to various cyber attacks that have a high impact on security and privacy. Detecting such attacks is crucial for implementing timely and effective countermeasures. Machine learning methods have been applied for intrusion detection tasks in various networks, but explaining the reasons for detection decisions remains an obstacle for security analysts. In this paper, we demonstrate that Transformer architecture, the core of the recent revolutionary large language models, constitutes a promising solution for intrusion detection in IoMT networks. We utilized a comprehensive dataset, CICIoMT2024, recently released specifically for these networks. We created a binary classification model for discriminating attacks from benign traffic and a multi-class model for the identification of specific attack types. We applied Explainable AI (XAi) methods such as LIME and SHAP to generate posthoc explanations for the model decisions. We evaluated and compared the quality of explanations based on three metrics: faithfulness, sensitivity, and complexity. Our findings demonstrate that the applied XAI methods enhance transparency in the predictions of Transformer-based intrusion detection models for IoMT networks, proving that both transparency and high performance can be achieved simultaneously. © 2024 IEEE.

关键词： Network intrusion

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：