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检索条件"任意字段=2021 International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2021"

共 8431 条记录，以下是81-90 订阅

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intelligent Intrusion Detection in IoT: Integrating machine learning and Feature Automation

Intelligent Intrusion Detection in IoT: Integrating Machine ...

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international conference on Computer and Automation engineering, ICCAE

作者： Nazratun Naim Neha A. A. M. Muzahid Md Shamsuzzoha Imrul Jahid Hua Han Yujin Zhang Md Musabbir Hossain Ferdous Sohel School of Electronic and Electric Engineering Shanghai University of Engineering Science Shanghai China Hawaii Natural Energy Institute University of Hawaii at Manoa Honolulu HI USA School of Information Technology Murdoch University Murdoch WA Australia

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

The rapid growth of the internet of things (IoT) raises serious security concerns, demanding effective protection from cyber-attacks. Intrusion detection depends on identifying key features, but despite advances in automation, manual feature selection remains necessary, limiting scalability. To address this limitation, we introduced a hybrid feature selection method that combines filter and wrapper techniques to automatically select important features and enhance the efficiency of machine learning (ML) models for intrusion detection tasks. We utilized the mutual information (MI) algorithm as the filter method and recursive feature elimination (RFE) as the wrapper method. We evaluated the performance of the proposed model on publicly available datasets, HIKARI2021 and UNSW-NB15. We compared the results with several existing methods, and our approach outperformed the state-of-the-art (SOTA) methods in terms of accuracy and training time. We presented comprehensive results, including both quantitative and qualitative analysis, to demonstrate the effectiveness and efficiency of our proposed methods.

关键词： Training Automation machine learning algorithms Limiting Intrusion detection Filtering algorithms Feature extraction Information filters Internet of Things Security

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A Computational Medical XR Discipline 41st

A Computational Medical XR Discipline

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41st Computer Graphics international conference, CGI 2024

作者： Papagiannakis, George Greenleaf, Walter Cole, Michael Zhang, Mark Datta, Rabi Delahaye, Mathias Grigoriou, Eleni Kamarianakis, Manos Protopsaltis, Antonis Bijlenga, Philippe Thalmann, Nadia Magnenat Tsiridis, Eleftherios Kenanidis, Eustathios Vamvakidis, Kyriakos Koutelidakis, Ioannis Kannape, Oliver A. Heraklion Greece ORamaVR S.A. Geneva Switzerland University of Crete Heraklion Greece Stanford University Stanford United States University of Michigan Medical School Ann Arbor United States Brigham and Women’s Hospital Harvard University Boston United States University Hospital of Cologne Cologne Germany Geneva University Hospitals Geneva Switzerland University of Western Macedonia Kozani Greece University of Geneva Geneva Switzerland MIRALab SARL Cologny Switzerland Aristotle University of Thessaloniki Thessaloniki Greece Imperial College London London United Kingdom Henry Dunant Hospital Athens Greece MindMaze S.A. Switzerland Lausanne Switzerland

ISBN: (纸本)9783031820205

Computational Medical Extended Reality ( CMXR ), brings together life sciences and neuroscience with mathematics, engineering, and computer science. It unifies computational science (scientific computing) with intelligent extended reality and spatial computing for the medical field. It significantly differs from previous "Clinical XR" and "Medical XR" terms, as it is focusing on how to integrate computational methods from neural simulation to computational geometry, computational vision and computer graphics with deep learning models to solve hard problems in medicine and neuroscience: from low-code/no-code/genAI authoring platforms to deep learning XR systems for training, planning, real-time operative navigation, therapeutics, and rehabilitation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models

A Structured Reasoning Framework for Unbalanced Data Classif...

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE international conference on

作者： Junliang Du Shiyu Dou Bohuan Yang Jiacheng Hu Tai An Shanghai Jiao Tong University Shanghai China Yale University New Haven USA University of California San Diego San Diego USA Tulane University New Orleans USA University of Rochester Rochester China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

This paper studies a Markov network model for unbalanced data, aiming to solve the problems of classification bias and insufficient minority class recognition ability of traditional machine learning models in environments with uneven class distribution. By constructing joint probability distribution and conditional dependency, the model can achieve global modeling and reasoning optimization of sample categories. The study introduced marginal probability estimation and weighted loss optimization strategies, combined with regularization constraints and structured reasoning methods, effectively improving the generalization ability and robustness of the model. In the experimental stage, a real credit card fraud detection dataset was selected and compared with models such as logistic regression, support vector machine, random forest and XGBoost. The experimental results show that the Markov network performs well in indicators such as weighted accuracy, F1 score, and AUC-ROC, significantly outperforming traditional classification models, demonstrating its strong decision-making ability and applicability in unbalanced data scenarios. Future research can focus on efficient model training, structural optimization, and deep learning integration in large-scale unbalanced data environments and promote its wide application in practical applications such as financial risk control, medical diagnosis, and intelligent monitoring.

关键词： Deep learning Support vector machines Adaptation models Decision making Distributed databases Probabilistic logic Data models Cognition Optimization Markov random fields

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Crime Prediction Based on Element Recognition and Entity Boundary Correction

Crime Prediction Based on Element Recognition and Entity Bou...

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE international conference on

作者： Siqi Xu Yan Gao School of Automation Central South University Changsha China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

In recent years, crime prediction has become a focal point in intelligent legal systems. Intellectual property crime prediction is especially complex due to its intricate criminal elements and the significant impact of amounts involved on sentencing. This paper introduces the first Intellectual property entity recognition dataset and proposes an innovative model for crime prediction based on element recognition and entity boundary correction. To tackle the issue of ambiguous boundaries in span identification during prediction, we incorporate a boundary detection task into joint training to refine entity boundaries. After recognizing entities and their categories, they are matched with organized rule tables to predict corresponding crime. Our proposed method demonstrates superior performance compared to other entity recognition models on this dataset.

关键词： Training Accuracy Law Federated learning Intellectual property Named entity recognition Predictive models Multitasking Consumer electronics

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A Consumer Study on the Acceptance of Digitized Packaging for Food Quality Assessment 58

A Consumer Study on the Acceptance of Digitized Packaging fo...

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58th Hawaii international conference on System Sciences, HICSS 2025

作者： Henrichs, Elia Senge, Julia Marie Krupitzer, Christian University of Hohenheim Department of Food Informatics and Computational Science Hub Germany

ISBN: (纸本)9780998133188

intelligent packaging can assist with reducing food waste and improving food safety. While indicators already provide the current food quality, sensors combined with machine learning techniques could predict the remaining shelf life and provide recommendations to maintain food quality. Thus, information from intelligent packaging must be transmitted and evaluated. However, successfully deploying such a concept requires the consumers' acceptance. Therefore, we conducted a consumer survey, investigating the attitudes and preferences of 242 participants regarding the use and connectivity of smarter technologies to evaluate food quality. The results revealed that 85% of the participants perceive a need for technological innovations to reduce food waste. Subsequently, we propose three approaches. Regarding information display, consumers prefer to show information directly on the packaging, via integrated displays in refrigerators, or a smartphone application. The information should provide the remaining shelf life, a scale, or a color indication. © 2025 IEEE Computer Society. All rights reserved.

关键词： consumer acceptance digital transformation food packaging food quality intelligent packaging

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Wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme learning machine 8th

Wind Power Forecasting Model Based on Feature Processing and...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wei, Xiongfei Wang, Jing Ruan, Yi Fang, Yuanjie College of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China School of Electronic Engineering Chaohu University Anhui Chaohu238024 China

ISBN: (纸本)9789819602315

To improve the forecasting accuracy of wind power, a wind power forecasting (WPF) model based on feature cleaning combined with deep hybrid kernel extreme learning machine (DHKELM) is proposed. The Pearson correlation coefficient (PCC) method is used to optimize the features, eliminate irrelevant features, and the isolated forest (IF) algorithm is used to clean the anomalous data in the retained features, which enhances the correlation between the selected features and wind power, and then combines the DHKELM to predict the wind power. Finally, the proposed model is applied to predict the wind power of a 1MW wind turbine, and the results of the progressive forecasting of the proposed method and the forecasting results of different models are compared. The results show that the PCC-IF-DHKELM forecasting model has a root-mean-square error of 0.01928 MW, an average absolute error of 0.015254 MW, and a correlation coefficient of 0.99435, which is significantly better than the other forecasting models, proving the effectiveness of the PCC-IF-DHKELM forecasting method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind forecasting

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Capsule Networks with a Self-Attention Mechanism for Classification of Parkinson's Disase from Tremor

Capsule Networks with a Self-Attention Mechanism for Classif...

引用

Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE international conference on

作者： Rongrong Ban School of Information Engineering Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

The traditional diagnosis of Parkinson's disease (PD)primarily relies on the Unified Parkinson's Disease Rating Scale and the clinical experience of physicians, leading to significant subjectivity in diagnosis. To address this issue, we employ sensor systems to collect tremor parameters from PD patients and analyzed these collected data using traditional machine learning algorithms, providing an objective and quantitative diagnosis for PD. To obtain more comprehensive features, this study adopts the PKG wearable device to extract 12 tremor features from 100 PD patients, along with recording 9 scale-based features and 3 demographic characteristics. Furthermore, to overcome the issues of low accuracy and poor generalization ability of traditional machine learning methods, we propose a capsule network integrated with a self-attention mechanism (SA-CapsNet) for classifying and predicting early stage and moderate to advanced stage stage PD patients. We also utilize the Synthetic Minority Over-sampling Technique (SMOTE) to improve the model's ability to generalize. Lastly, The Shapley Additive exPlanations (SHAP) model is introduced to improve the interpretability of the predictive model, and feature analysis is conducted to identify the main factors influencing classification and prediction. Experimental results demonstrate that the SA-CapsNet method for PD can achieve an accuracy of 92.31%, precision of 82.19%, recall of 91.23%, specificity of 80.00%, and an F1 score of 85.25%. Therefore, the proposed SACapsNet method not only exhibits good generalization ability for small sample sizes and imbalanced class data but also effectively assists physicians in staging the severity of PD.

关键词： Support vector machines Accuracy Parkinson's disease Medical services Predictive models Feature extraction Sensor systems Data mining Wearable devices Wearable sensors

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machine learning-Based Power Loss Analysis in Photovoltaic systems

Machine Learning-Based Power Loss Analysis in Photovoltaic S...

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Smart and Sustainable Developments in Electrical engineering (SSDEE), IEEE international conference on

作者： Anuradha Tomar Hritik Kumar Department of Instrumentation & Control Engineering Netaji Subhas University of Technology

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

This research estimates the power loss in photovoltaic (PV) systems through a Random Forest model by using 15-min interval data for two years (2021–2023) of operation from a university campus located in Delhi, India. Irradiance, temperature and the dust concentration were used in the analysis to calculate and estimate actual and theoretical potentials and power losses based on factual, modelled and theoretical determinations under Standard test conditions (STC). Seasonal changes are of particular interest and therefore, the power loss is to be analyzed based on three power quality reports obtained over three different seasons in 2023. The results of the model show the Mean absolute error (MAE) to be 29.145 W along with Root mean square error (RMSE) to be 57.72 W. Interactions with power loss observed seasonally proved to have variations in dust and power loss, temperature, and so on; all of which confirmed the effectiveness of Random Forest model in providing valuable predictions of season-sensitive data. These results would be significant to enhance the efficiency of the PV system and how energy technologies for dusty areas should be designed.

关键词： Photovoltaic systems Temperature distribution Sensitivity System performance Predictive models Data models Sustainable development Root mean square Random forests Standards

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12th international Workshop on Biological Knowledge Discovery from Data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical systems, IWCFS 2021, 3rd international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered Data, ProTime2021 and 1st international Workshop on AI System engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on Database and Expert systems Applications, DEXA 2021

12th International Workshop on Biological Knowledge Discover...

引用

12th international Workshop on Biological Knowledge Discovery from Data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical systems, IWCFS 2021, 3rd international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered Data, ProTime2021 and 1st international Workshop on AI System engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on Database and Expert systems Applications, DEXA 2021

ISBN: (纸本)9783030871000

The proceedings contain 23 papers. The special focus in this conference is on Database and Expert systems Applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works;robust and Efficient Bio-Inspired Data-Sampling Prototype for Time-Series Analysis;membership-Mappings for Data Representation learning: Measure Theoretic Conceptualization;membership-Mappings for Data Representation learning: A Bregman Divergence Based Conditionally Deep Autoencoder;data Catalogs: A Systematic Literature Review and Guidelines to Implementation;task-Specific Automation in Deep learning Processes;approximate Fault Tolerance for Edge Stream Processing;deep learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends;time Series Pattern Discovery by Deep learning and Graph Mining;a Conceptual Model for Mitigation of Root Causes of Uncertainty in Cyber-Physical systems;integrating Gene Ontology Based Grouping and Ranking into the machine learning Algorithm for Gene Expression Data Analysis;SVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-R;short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles;a Comparative Study of Deep learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet;Security-Based Safety Hazard Analysis Using FMEA: A DAM Case Study;Privacy Preserving machine learning for Malicious URL Detection;remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor;Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance;placeholder Constraint Evaluation in Simulation Graphs;Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs;bridging Semantic Web and machine learning: First Results of a Systematic Mapping Study.

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Coughing and Seasonality: A Study Based on machine learning and Internet Data Analysis

Coughing and Seasonality: A Study Based on Machine Learning ...

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Electronics, Energy systems and Power engineering (EESPE), IEEE international conference on

作者： Xiangwei Meng Fengxia Wu Dedong Ma Fulai Peng Ningling Zhang Danyang Lv Zhi Li Tiefeng Sun Chongxuan Tian Yang Shen Cai Chen Biomedical Engineering Institute School of Control Science and Engineering Shandong University Jinan China School of Basic Medical Sciences Shandong University Jinan China Department of Pulmonary and Critical Care Medicine Qilu Hospital Shandong University Jinan China Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan China Shandong Academy of Chinese Medicine Jinan China Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

Cough is a defensive physiological response, serving as a protective reflex of the respiratory system against irritants. The incidence of cough varies across different seasons. Although it's a general consensus, there is currently insufficient data to fully support this conclusion. This study aims to examine the relationship between cough and seasonal transitions using internet-based data based on machine learning. Meteorological data, air pollution data, and search index data for coughing were collected from Internet, with research area in Jinan, China from January 2014 to December 2021. Spearman's rank correlation coefficient was utilized to explore the associations between the aforementioned variables. A distributed lag non- linear model was employed to assess the impact of air pollution on the search index data for cough. Time series annual and monthly decomposition models were utilized to analyze the association between the search index for coughing and seasonal variations. The risk factors of cough were analyzed by machine learning model. The results demonstrated a connection between the search index for coughing and seasonal changes, with coughing reaching its peak during cold seasons. Specifically, the search index for cough showed an upward trend from July to January of the following year, while it experienced a downward trend from January to June. The monthly decomposition results further confirmed that January represents the peak period. Moreover, between 2015 and 2018, the search index for cough was higher compared to other years. The seasonal trend analysis reveals a close correlation between cough and the seasons, with cough reaching its peak during cold seasons. The results of six machine learning models and generalized additive models showed that meteorological factors had a typical delayed effect on coughing.

关键词： Analytical models Correlation Meteorological factors Atmospheric modeling Time series analysis machine learning Market research Data models Internet Indexes

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