检索结果-内蒙古大学图书馆

3rd international conference on Optimization, learning Algorithms and Applications (OL2A)

作者： Lisitsa, Alexei Vernitski, Alexei Univ Liverpool Dept Comp Sci Liverpool Merseyside England Univ Essex Dept Math Sci Colchester Essex England

ISBN: (纸本)9783031530357;9783031530364

On some data, machine learning displays anti-learning;that is, while the classifier demonstrates excellent performance on the training set, it performs much worse than the random classifier on the test set. In this paper we study what we call strong anti-learning, that is, the most surprising scenario, in which the more examples you place in the training set, the worse the accuracy becomes, until it becomes 0% on the test set. We produce a framework in which strong anti-learning can be reproduced and studied theoretically. We deduce a formula estimating anti-learning when decision trees (one of the most important tools of machine learning) solve the parity bit problem (one of the most famously tricky problems of machine learning). Our estimation formula (deduced under certain mathematical assumptions) agrees very well with experimental results (produced on random data without these assumptions).

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Harnessing machine learning for Predictive Analytics: A Case Study of Lassa Fever Outbreaks in Nigeria 10

Harnessing Machine Learning for Predictive Analytics: A Case...

引用

10th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Quezada, Daniel A. Akwafuo, Sampson Halyal, Samarth Calif State Univ Fullerton Dept Comp Sci Fullerton CA 92634 USA

ISBN: (纸本)9798350373981;9798350373974

In the ongoing battle against global pandemics, understanding the key determinants that fuel outbreaks are of paramount importance. With this focus, our study aims to assess and rank the predictive capabilities of a wide range of socio-economic, eco-climatic, and spatiotemporal variables in predicting Lassa Fever (LF) outbreaks, using data from previous Nigerian outbreaks (2012-2019). Employing machine learning methods, particularly XGBoost and Random Forest, our study aims to offer accurate and robust predictions concerning LF incidence rates. As a crucial add-on, we leverage the innovative SHAP (SHapley Additive exPlanations) technique as a postprocessing tool to dissect and better understand the contributions of individual features towards the predictions generated by our machine learning models. This multi-layered approach allowed us to place a pronounced focus on healthcare infrastructure, population demographics, land cover, and other climatic co-variates. Among the models evaluated, XGBoost performed the best;delivering an accuracy of 0.93, and AUC of 0.90, and an F1 score of 0.86 on 2018 data. For 2019 data, it maintained a strong accuracy of 0.90, an AUC of 0.89, and an F1 score of 0.75. Our SHAP analysis further emphasized precipitation seasonality, diagnostic center density, and land cover characteristics as pivotal influencers in predicting LF outbreaks. These findings shed light on the complex interplay between environmental conditions, urbanization, and healthcare infrastructure. Given these promising results, our work sets the stage for the development of an advanced early warning system for Lassa Fever in Nigeria: a system that could efficiently intertwine computational insights with on-ground interventions, ensuring timely and targeted responses to potential outbreaks.

关键词： Lassa Fever Computational Epidemiology machine learning SHAP Africa Public Health Nigeria

来源：评论

学校读者我要写书评

暂无评论

Elevating nanomaterial optical sensor arrays through the integration of advanced machine learning techniques for enhancing visual inspection of food quality and safety

引用

CRITICAL REVIEWS IN FOOD science AND NUTRITION 2024年 1-22页

作者： Lin, Yuandong Cheng, Jun-Hu Ma, Ji Zhou, Chenyue Sun, Da-Wen South China Univ Technol Sch Food Sci & Engn Guangzhou Peoples R China South China Univ Technol Acad Contemporary Food Engn Guangzhou Higher Educ Mega Ctr Guangzhou Peoples R China Guangzhou Higher Educ Mega Ctr Engn & Technol Res Ctr Guangdong Prov Intelligent Guangzhou Peoples R China Guangzhou Higher Educ Mega Ctr Guangdong Prov Engn Lab Intelligent Cold Chain Log Guangzhou Peoples R China Univ Coll Dublin Natl Univ Ireland Agr & Food Sci Ctr Food Refrigerat & Computerized Food Technol FRCFT Belfield Ireland

Food quality and safety problems caused by inefficient control in the food chain have significant implications for human health, social stability, and economic progress and optical sensor arrays (OSAs) can effectively address these challenges. This review aims to summarize the recent applications of nanomaterials-based OSA for food quality and safety visual monitoring, including colourimetric sensor array (CSA) and fluorescent sensor array (FSA). First, the fundamental properties of various advanced nanomaterials, mainly including metal nanoparticles (MNPs) and nanoclusters (MNCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and others, were described. Besides, the diverse machine learning (ML) and deep learning (DL) methods of high-dimensional data obtained from the responses between different sensing elements and analytes were presented. Moreover, the recent and representative applications in pesticide residues, heavy metal ions, bacterial contamination, antioxidants, flavor matters, and food freshness detection were comprehensively summarized. Finally, the challenges and future perspectives for nanomaterials-based OSAs are discussed. It is believed that with the advancements in artificial intelligence (AI) techniques and integrated technology, nanomaterials-based OSAs are expected to be an intelligent, effective, and rapid tool for food quality assessment and safety control.

关键词： Optical sensor arrays nanomaterials quality assessment safety control machine learning intelligent detection

来源：评论

学校读者我要写书评

暂无评论

Siamese Autoencoder Architecture for the Imputation of data Missing Not at Random

引用

JOURNAL OF COMPUTATIONAL science 2024年 78卷

作者： Pereira, Ricardo Cardoso Abreu, Pedro Henriques Rodrigues, Pedro Pereira Univ Coimbra Ctr Informat & Syst Dept Informat Engn P-3030290 Coimbra Portugal Univ Porto Fac Med MEDCIDS Ctr Hlth Technol & Serv Res P-4200319 Porto Portugal

Missing data is an issue that can negatively impact any task performed with the available data and it is often found in real -world domains such as healthcare. One of the most common strategies to address this issue is to perform imputation, where the missing values are replaced by estimates. Several approaches based on statistics and machine learning techniques have been proposed for this purpose, including deep learning architectures such as generative adversarial networks and autoencoders. In this work, we propose a novel siamese neural network suitable for missing data imputation, which we call Siamese Autoencoder-based Approach for Imputation (SAEI). Besides having a deep autoencoder architecture, SAEI also has a custom loss function and triplet mining strategy that are tailored for the missing data issue. The proposed SAEI approach is compared to seven state-of-the-art imputation methods in an experimental setup that comprises 14 heterogeneous datasets of the healthcare domain injected with Missing Not At Random values at a rate between 10% and 60%. The results show that SAEI significantly outperforms all the remaining imputation methods for all experimented settings, achieving an average improvement of 35%. This work is an extension of the article Siamese Autoencoder-Based Approach for Missing data Imputation [1] presented at the international conference on Computational science 2023. It includes new experiments focused on runtime, generalization capabilities, and the impact of the imputation in classification tasks, where the results show that SAEI is the imputation method that induces the best classification results, improving the F1 scores for 50% of the used datasets.

关键词： Missing data Imputation Siamese autoencoder Missing Not at Random

来源：评论

学校读者我要写书评

暂无评论

FedFair³: Unlocking Threefold Fairness in Federated learning 59

FedFair<SUP>3</SUP>: Unlocking Threefold Fairness in Federat...

引用

59th Annual IEEE international conference on Communications (IEEE ICC)

作者： Javaherian, Simin Panta, Sanjeev Williams, Shelby Islam, Md Sirajul Chen, Li Univ Louisiana Lafayette Sch Comp & Informat Lafayette LA 70504 USA

ISBN: (纸本)9781728190549

Federated learning (FL) is an emerging paradigm in machine learning without exposing clients' raw data. In practical scenarios with numerous clients, encouraging fair and efficient client participation in federated learning is of utmost importance, which is also challenging given the heterogeneity in data distribution and device properties. Existing works have proposed different client-selection methods that consider fairness;however, they fail to select clients with high utilities while simultaneously achieving fair accuracy levels. In this paper, we propose a fair client-selection approach that unlocks threefold fairness in federated learning. In addition to having a fair client-selection strategy, we enforce an equitable number of rounds for client participation and ensure a fair accuracy distribution over the clients. The experimental results demonstrate that FedFair(3), in comparison to the state-of-the-art baselines, achieves 18.15% less accuracy variance on the IID data and 54.78% on the non-IID data, without decreasing the global accuracy. Furthermore, it shows 24.36% less wall-clock training time on average.

关键词： Accuracy Convergence Analysis Fairness Federated learning Importance Sampling Participant Selection

来源：评论

学校读者我要写书评

暂无评论

Context-Aware Hard and Slow Fall Detection 20

Context-Aware Hard and Slow Fall Detection

引用

20th IEEE international Wireless Communications and Mobile Computing (IEEE IWCMC) - Green and Intelligent Communications

作者： Besrour, Sinda Mubibya, Gael S. Liu, Zikuan Almhana, Jalal Univ Moncton Dept Comp Sci Moncton NB Canada Univ New Brunswick Fredericton NB Canada

ISBN: (纸本)9798350361261;9798350361278

Fall is one of the main causes of injuries for the elderly, and fall detection (FD) for senior monitoring has received considerable attention from both the academic community and healthcare industries. In recent years, there has been an increasing interest in using wearable sensors, such as accelerometers to monitor the subject's body movement and apply machine learning (ML) methods to detect and prevent falls. Since it is extremely difficult to collect accelerometer data of real falls during activities of daily living (ADL), researchers tended to rely on simulating falls in well-protected environments. They collected ADLs separately, applied ML algorithms to classify falls and ADLs, and reported very high FD accuracy rates. However, these studies cannot be applied in a real fall context. In this paper, instead of classifying ADL and fall separately, we propose to incorporate fall data within ADL data to obtain more realistic datasets and apply ML to detect falls. Several ML algorithms including CatBoost (CB), Decision Tree (DT), Random Forest (RF), and XGBoost (XGB) were applied to the datasets. Experimental results show a fall detection accuracy of 88.70%. We also extend our work to cover slow fall which, to the best of our knowledge, was not extensively addressed in previous works.

关键词： Realistic-Context Awareness Fall Detection IoT Medical Applications Wireless Sensors Accelerometer machine learning

来源：评论

学校读者我要写书评

暂无评论

Research on the Detection Methods for Changes in the Mangroves of Dongzhaigang, Hainan from 2019 to 2023 24

Research on the Detection Methods for Changes in the Mangrov...

引用

8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Xianyao Lu, Huajun Qiu, Shuailong Liu, Lijin Wu, Zhongqiang School of Information Science and Technology Hainan Normal University Hainan Haikou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400710094

This study introduces the research methods for detecting changes in the mangroves of Dongzhaigang, Hainan from 2019 to 2023. Mangrove ecosystems play a crucial role in providing habitats and have ecological and economic value, but in recent years, they have faced degradation and management issues. In order to effectively monitor mangrove changes, researchers have utilized remote sensing techniques and machine learning methods for mangrove cover information extraction and classification. The paper describes the methods of estimating mangrove cover using remote sensing satellite imagery and spectral indices and explores the applications of clustering, decision trees, support vector machines, and deep learning in mangrove change detection. The research results indicate that the support vector machine algorithm performs well in classifying mangrove and non-mangrove areas. This study provides a basis for the management and decision-making of Dongzhaigang Mangrove Nature Reserve in Hainan Province and highlights the future research potential in the application of multi-source and multi-resolution data and deep learning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

machine learning applied to social science research: The Nexus of Internet Access, Economic Freedom, and Corruption 15

Machine learning applied to social science research: The Nex...

引用

15th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th international conference on Emerging data and Industry 4.0, EDI40 2024

作者： Picon, Cristian Ariza, Marco Antonio Monroy, Estefanny Albor, Gustavo Rodriguez Universidad de la Costa CUC Barranquilla08003 Colombia Universidad Libre Seccional Barranquilla Barranquilla08003 Colombia Universidad Autonoma Del Caribe Barranquilla080020 Colombia

This study employed a quantile regression model as a machine learning tool in Python to assess the nuanced effects and relationships within a dataset examining the combined impact of Internet connectivity and degrees of economic freedom on global corruption. The study uses a dataset covering 161 nations from 2010 to 2019, incorporating various sources of information for a range of variables rooted in theory. The findings suggest an influence of the combination of greater freedom and internet access in countering corruption. Consequently, policies that focus on improving the availability of open information, online social networking, and other elements linked to Internet accessibility should be considered as a practical approach to curbing corruption within the countries examined. © 2024 Elsevier B.V.. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

data Drift for Automatic FAIR-compliant dataset Versioning in Large Repositories 20

Data Drift for Automatic FAIR-compliant Dataset Versioning i...

引用

20th IEEE international conference on E-science (E-science)

作者： Gonzalez-Cebrian, Alba Ciolacu, Iulian Bradford, Michael Dobre, Ciprian Gonzalez-Velez, Horacio Natl Coll Ireland Cloud Competency Ctr Dublin Ireland Univ Politehn Bucuresti Fac Automat Control & Comp Bucharest Romania

ISBN: (纸本)9798350365627;9798350365610

Construed as a shift in the distribution or structure of data over time, data drift can adversely affect the performance of machine learning models and data-driven decisions. This study examines two data drift metrics, denoted as d(E,PCA) and d(E,AE), that are derived from unsupervised ML models: the reconstruction error-based metrics of Principal Component Analysis (PCA) and Autoencoders (AE). To investigate the robustness of these metrics, we have systematically accessed time-series datasets from the European data Portal. Our experiments have examined data versioning through three basic events: creation, update, and deletion. The results are summarised and aggregated for all datasets, and unsupervised analysis based on Robust PCA and AE has been performed to examine patterns within the impact of dataset characteristics on data drift detection and computational efficiency. Our results indicate that both metrics aligned closely in performance with new records, suggesting consistent drift detection under normal conditions with FAIR compliance. However, high-dimensional datasets posed challenges for both PCA and AE models. Update events revealed discrepancies between the two metrics, suggesting that non-linear shifts affected AE-based metrics more than PCA-based ones. Deletion events demonstrated the resilience of these metrics against data loss, but also revealed variability in the reliability of the PCA model;i.e., data drift metrics derived from PCA and AE can be effective but sensitive to certain dataset characteristics.

关键词： data drift machine learning FAIR principles Principal Component Analysis Autoencoders data versioning Time series dataset Versioning

来源：评论

学校读者我要写书评

暂无评论

Optimizing DDoS Attack Detection Using machine learning 2

Optimizing DDoS Attack Detection Using Machine Learning

引用

2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aggarwal, Saransh Behera, Bhagrajyoti Singh, Murari Kumar Sharma, Ajeet Kumar School of Computing Science and Engineering Sharda University Greater Noida India School of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack. © 2025 IEEE.

关键词： Network security

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：