检索结果-内蒙古大学图书馆

10th IEEE International Conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Aaptha, B., V Kadali, Phani Gupta, Saurabh Saju, Vinolia Continental Autonomous Mobil India Pvt Ltd Radar Percept Grp Bangalore Karnataka India

ISBN: (纸本)9798350385939;9798350385922

In recent years, the increasing demand for technological advances has driven the usage of state-of-the-art technologies like machine learning in automated systems. In this study, a novel framework, Km-IFC, is presented that effectively estimates the course of the road using radar detections, and its results are discussed. It uses a combination of the K-means clustering algorithm for separation of data, the isolation forest algorithm for removal of outliers, and the least squares method to estimate the curve parameters of the roads' course. The estimated curve using radar data is compared with the vehicle-driven path (considered as ground truth). Km-IFC achieved favorable results in both optimal and non-optimal scenarios, with almost negligible differences between the estimated curve and ground truth. Consequently, the estimated curve had an average difference of 0.365 meters and an overall confidence coefficient of 0.88, inclusive of all scenarios. Furthermore, the study conducted takes in raw sensor detection data and estimates the roads' course without relying on any external sensor outputs, thus making it very computationally efficient.

关键词： K-means clustering algorithm isolation forest algorithm Curve fitting Radar detections Road trajectory

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Analysis for Fraud Detection of Credit Card through Machine Learning

Comprehensive Analysis for Fraud Detection of Credit Card th...

引用

3rd IEEE International Conference on Emerging Smart Computing and Informatics (IEEE-ESCI)

作者： Roy, Parth Rao, Prateek Gajre, Jay Katake, Kanchan Jagtap, Arvind Gajmal, Yogesh MIT ADT Univ Informat Technol Dept MIT Sch Engn Pune Maharashtra India

ISBN: (纸本)9781728185194

A credit card which remains a very widespread compensation method is accepted online & offline that provides cashless transactions. It's an easy, suitable then very common to make payments and other transactions. With the increase of developments credit card frauds are also growing. Financial deception is severely cumulative in the worldwide statement enhancement. Billion dollars are at loss due to these fraudulent acts. These actions are accomplished so gracefully that it is similar to genuine transactions. Therefore, simple design practices and other less composite methods will be nonoperating. In directive to minimalize disorder and bring order in place having a well-organized method of fraud detection has become a need for all banks. In this paper we used Machine learning, to notice Master Card fake transactions. Also, IFA and OD approaches are applied towards enhance finest answer on behalf of scam finding problems. Approaches remain proved toward diminish untrue alarm proportions also upsurge scam discovery proportion. Dataset of card dealings stays obtained since European card owners having 284,807 communications. To detect and prevent the fraudulent, slightly of these approaches can be applied on bank credit card scam detection system, to detect and prevent the scam.

关键词： isolation forest algorithm Credit card fraud Local Outlier Factor Machine Learning Logistic Regression

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Dark Patterns in E-Commerce Web Sites 5

Unveiling the Dark Patterns in E-Commerce Web Sites

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kirthiga, N. Reddy, Bommana Manideep Mahalakshmi, Boggavarapu Siri Risthitha, Bethala Teja, Dammalapa Kalasalingam Academy of Research and Education Departement of Computer Science and Engineering Tamil Nadu Virudhunagar India

ISBN: (纸本)9798331530013

This research study investigates the prevalence and impact of 'dark patterns' in the digital world. Dark patterns are deceptive design techniques employed by websites and applications to manipulate user behavior and induce actions that may not be in the user's best interest. This study examines the various forms of dark patterns, analyzes their impact on user autonomy and trust, and explores potential strategies to mitigate their use. By raising awareness about dark patterns and developing effective countermeasures, we can create a more ethical and trustworthy digital environment for all users. © 2024 IEEE.

关键词： Dark Patterns Data Exploration Digital Ethics isolation forest algorithm User Experience

来源：评论

学校读者我要写书评

暂无评论

Detection and Tracking of Atypical Billings Aiming at Minimizing Non-Technical Losses in Distribution Companies in Brazil

Detection and Tracking of Atypical Billings Aiming at Minimi...

引用

2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Bettiol, Arlan Luiz Gomides, Phablo Sullyvan Figueiredo, Douglas Barbonaglia Sathler Medeiros, Renato Neo Domino Pesquisa em Sistemas Elétricos Santa Catarina Florianópolis Brazil Companhia Hidrelétrica São Patrício Goiás Ceres Brazil Empresa Luz e Força Santa Maria Espírito Santo Colatina Brazil

ISBN: (纸本)9798350327816

The work addresses the development of a computational platform to identify atypical billings in low voltage consumer units using Artificial Intelligence techniques, specifically the isolation forest algorithm. The proposed methodology allows detecting consumer units with peculiar changes in consumption and/or evidence of fraud. The algorithm analyzes a set of historical data and identifies 'atypical' monthly billings among consumer units with the same load profile, generating a list of candidate units for subsequent inspection in the field. Preliminary tests carried out in a medium-sized distribution company showed an assertiveness rate of 91.70%, indicating the effectiveness of the proposed algorithm for identifying anomalies in the monthly billing data of consumer units. © 2023 IEEE.

关键词： Artificial intelligence Atypical Billings Combating Commercial Losses Commercial Loss Detection isolation forest algorithm

来源：评论

学校读者我要写书评

暂无评论

A method for battery fault diagnosis and early warning combining isolated forest algorithm and sliding window

引用

ENERGY SCIENCE & ENGINEERING 2023年第12期11卷 4493-4504页

作者： Cheng, Xianfu Li, Xiaojing Ma, Xiaodong East China Jiaotong Univ Sch Mechatron & Vehicle Engn Room 6 234 808 Shuanggang East Rd Nanchang 330013 Jiangxi Peoples R China

The vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real-time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults. This paper proposes a method for battery fault diagnosis and early warning combining isolated forest (IF) algorithm and sliding window (SW), which uses the SW to segment the dataset and update the data of the diagnosis model in real-time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75, then the IF algorithm is used to identify and warn the abnormal ***

关键词： fault diagnosis isolation forest algorithm power battery sliding windows

来源：评论

学校读者我要写书评

暂无评论

Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China's Diverse Latitudes

引用

REMOTE SENSING 2023年第11期15卷 2951-2951页

作者： Zhang, Haiyang Zhang, Yao Gao, Tingyao Lan, Shu Tong, Fanghui Li, Minzan China Agr Univ Key Lab Smart Agr Syst Integrat Minist Educ Beijing 100083 Peoples R China

Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, up until now, there has been no proposed remote sensing fusion product that offers high spatial-temporal resolution specifically for farmland monitoring. Therefore, focusing on the demands of farmland remote sensing monitoring, identifying quantitative conversion relationships between multiple sensors, and providing high spatial-temporal resolution products is the first step that needs to be addressed. In this study, a fused Landsat 8 (L8) Operational Land Imager (OLI) and Sentinel-2 (S-2) multi-spectral instruments (MSI) data product for regional monitoring of farmland at high, mid, and low latitudes in China is proposed. Two image pairs for each study area covering different years were acquired from simultaneous transits of L8 OLI and S-2 MSI sensors. Then, the isolation forest (iforest) algorithm was employed to remove the anomalous pixels of image pairs and eliminate the influence of anomalous data on the conversion relationships. Subsequently, the adjustment coefficients for multi-source sensors at mixed latitudes with high spatial resolution were obtained using an ordinary least squares regression method. Finally, the L8-S-2 fused dataset based on the adjustment coefficients is proposed, which is suitable for different latitude farming areas in China. The results showed that the iforest algorithm could effectively improve the correlation between the corresponding spectral bands of the two sensors at a spatial resolution of 10 m. After the removal of anomalous pixels, excellent correlation and consistency were obtained in three study areas, and the Pearson correlation coefficients between the corresponding spectral bands almost all exceeded 0.88. Furthe

关键词： Sentinel-2 MSI Landsat 8 OLI Pearson correlation coefficient ordinary least squares regression isolation forest algorithm sensor integration

来源：评论

学校读者我要写书评

暂无评论

Anomaly electricity detection method based on entropy weight method and isolated forest algorithm

引用

FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Wang, Jianyuan Gu, Chengcheng Liu, Kechen Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin Peoples R China

This study aims at investigating the applicability of abnormal electricity consumption data detection method, which is based on the entropy weight method and the isolated forest tree algorithm. The inaccessibility and imbalance of abnormal electricity consumption samples in actual data sets are considered by analyzing smart distribution network power consumption big data. Firstly, the users are classified by the k-means clustering algorithm, and then the characteristics of each type of user are extracted and the feature set is processed by the principal component analysis method to reduce the dimensions, followed by the entropy weight method adaptive configuration of the weight coefficients of each feature index, and finally the abnormal power consumption users are calculated based on the feature-weighted isolated forest algorithm. The algorithm verifies the real electricity consumption data of 6,445 users, and the results show that the method has a high detection accuracy, recall rate and F1 score, which is more suitable for the detection of abnormal electricity consumption in scenarios when there are complex and diverse user power consumption behaviors.

关键词： abnormal power consumption detection isolation forest algorithm kmeans clustering algorithm entropy weight method principal component dimension 11

来源：评论

学校读者我要写书评

暂无评论

An integrated data-driven framework for surface water quality anomaly detection and early warning

引用

JOURNAL OF CLEANER PRODUCTION 2020年 251卷 119145-000页

作者： Liu, Jie Wang, Peng Jiang, Dexun Nan, Jun Zhu, Weiyu Harbin Inst Technol Sch Environm State Key Lab Urban Water Resource & Environm Harbin 150090 Peoples R China Harbin Univ Sch Informat Engn Harbin 150086 Peoples R China

The surface water quality anomaly detection for rapid early warning is essential to prevent potential harmful compounds, resulting from river environmental spills or intentional releases, from dispersing in large scale. In this study, an effective data-driven framework for surface water quality anomaly detection is developed to provide early warnings for dealing with river environmental pollution in advance. The developed framework is constructed by an integration of Bayesian autoregressive (BAR) model for water quality variation prediction and isolation forest (IF) algorithm for water quality anomaly detection. First, an autoregressive method based on Bayesian inference is used to forecast the tendencies of water quality variations. Second, an IF algorithm is applied to identify the features of water quality anomalies using the prediction residuals obtained in the previous stage. The integration framework is then applied to analyze and detect the surface water quality variations and anomalies in Potomac River of West Virginia, USA, comparing with prediction-based anomaly detection method, classification-based anomaly detection method, and different scenarios. The results demonstrate that the developed integration framework not only could enhance water quality anomaly detection accuracy, but also effectively provide early warning for emergency operations in a quick response. (C) 2019 Published by Elsevier Ltd.

关键词： Surface water quality Anomaly detection Early warning Bayesian autoregressive model isolation forest algorithm

来源：评论

学校读者我要写书评

暂无评论

A Method for the Detection of Fake Reviews based on Temporal Features of Reviews and Comments

A Method for the Detection of Fake Reviews based on Temporal...

引用

3rd International Conference on Mechatronics Engineering and Information Technology(ICMEIT 2019)

作者： Wenqian Liu Jingsha He Song Han Nafei Zhu Faculty of Information Technology Beijing University of Technology

Online reviews and comments have become an important resource for various decision making processes, such as sale and buy decisions. The truthfulness of online reviews is thus critical for both buyers and sellers since fake reviews will affect customer's decisions due to misleading description and deceptive selling. This can cause financial loses for innocent customers. Fake review detection has thus attracted a lot of attention. However, most shopping websites have only focused on dealing with problematic reviews and comments. In this paper, we propose a method for the detection of outlier reviews based on reviewing records associated with products instead of just the reviews and comments. We first analyze the characteristics of such data using a crawled Amazon China dataset, revealing that the reviewing records of each product is similar for normal products. In the proposed method, we first extract the reviewing records of products to a temporal feature vector. We then develop an isolation forest algorithm to detect the outlier reviews of products based on the reviewing records of reviews and comments. We will verify the effectiveness of our proposed method and compare it to some existing temporal outlier detection methods using the crawled Amazon China dataset. We will also study the impact caused by the parameter selection of the reviewing records.

关键词： fake reviews products speculation reviewing records isolation forest algorithm

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：