检索结果-内蒙古大学图书馆

Web server log data pre-processing for mining zakat user profile using association rules

International Journal of Business Intelligence and data Mining 2025年第1-2期26卷 46-65页

作者： Mohamad Mohsin, Mohamad Farhan Wan Ishak, Wan Hussain Yusof, Yuhanis Jamil, Jastini Mohd Ahmad, Alwi School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia School of Quantitative Sciences Universiti Utara Malaysia Kedah Sintok06010 Malaysia Lembaga Zakat Negeri Kedah Menara Zakat Jalan Teluk Wanjah Kedah Alor Setar05200 Malaysia

The internet’s transformative impact on businesses and marketing strategies underscores the pivotal role of websites in establishing credibility and disseminating information to customers. To measure website effectiveness, tracking visitor behaviour is essential. This study focuses on web log data from Lembaga Zakat Negeri Kedah (LZNK), a Malaysian government institution managing zakat, which utilises web analytics and mining to gain insights into website usage. The objectives of this paper are two-fold: firstly, to detail the pre-processing of web log data to ensure reliability for data mining. Secondly is to employ association rule mining to extract user patterns from pre-processed web log data. To achieve this, the web logs were obtained from the LZNK’s website spanning from 2016 to November 2020 with a focus on user access in 2020. The findings reveal critical aspects of user behaviour including the most visited pages, popular page combinations, user interests, relationships between pages, and the impact of the entry page. Implementing these insights can enhance the LZNK website’s usability, user satisfaction, and highlighting the importance of adapting to evolving user preferences and technological advancements. © 2025 Inderscience Enterprises Ltd.

关键词： association rule data pre-processing user profile web log web mining

来源：评论

学校读者我要写书评

暂无评论

data pre-processing Method to Remove Interference of Gas Bubbles and Cell Clusters During Anaerobic and Aerobic Yeast Fermentations in a Stirred Tank Bioreactor

引用

JOURNAL OF APPLIED SPECTROSCOPY 2014年第5期81卷 855-861页

作者： Princz, S. Wenzel, U. Miller, R. Hessling, M. Univ Appl Sci Ulm Inst Med Engn & Mechatron D-89081 Ulm Germany

One aerobic and four anaerobic batch fermentations of the yeast Saccharomyces cerevisiae were conducted in a stirred bioreactor and monitored inline by NIR spectroscopy and a transflectance dip probe. From the acquired NIR spectra, chemometric partial least squares regression (PLSR) models for predicting biomass, glucose and ethanol were constructed. The spectra were directly measured in the fermentation broth and successfully inspected for adulteration using our novel data pre-processing method. These adulterations manifested as strong fluctuations in the shape and offset of the absorption spectra. They resulted from cells, cell clusters, or gas bubbles intercepting the optical path of the dip probe. In the proposed data pre-processing method, adulterated signals are removed by passing the time-scanned non-averaged spectra through two filter algorithms with a 5% quantile cutoff. The filtered spectra containing meaningful data are then averaged. A second step checks whether the whole time scan is analyzable. If true, the average is calculated and used to prepare the PLSR models. This new method distinctly improved the prediction results. To dissociate possible correlations between analyte concentrations, such as glucose and ethanol, the feeding analytes were alternately supplied at different concentrations (spiking) at the end of the four anaerobic fermentations. This procedure yielded low-error (anaerobic) PLSR models for predicting analyte concentrations of 0.31 g/l for biomass, 3.41 g/l for glucose, and 2.17 g/l for ethanol. The maximum concentrations were 14 g/l biomass, 167 g/l glucose, and 80 g/l ethanol. data from the aerobic fermentation, carried out under high agitation and high aeration, were incorporated to realize combined PLSR models, which have not been previously reported to our knowledge.

关键词： data pre-processing inline bioprocess monitoring NIR spectroscopy anaerobic and aerobic yeast fermentation chemometrics scattering medium

来源：评论

学校读者我要写书评

暂无评论

data pre-processing to Train a Better Lithuanian-English MT System

Data Pre-Processing to Train a Better Lithuanian-English MT ...

引用

5th International Conference on Human Language Technologies - The Baltic Perspective (Baltic HLT)

作者： Deksne, Daiga Skadins, Raivis TILDE Riga Latvia

In this paper, we present the results of a series of experiments done to improve the quality of a Lithuanian-English statistical MT (SMT) system. We particularly focus on word alignment and out of vocabulary issues in... 详细信息

ISBN: (纸本)9781614991335;9781614991328

关键词： statistical MT data pre-processing Lithuanian-English

来源：评论

学校读者我要写书评

暂无评论

data pre-processing Based on Convolutional Neural Network for Improving precision of Indoor Positioning 12th

Data Pre-processing Based on Convolutional Neural Network fo...

引用

12th Asian Conference on Intelligent Information and database Systems (ACIIDS)

作者： Lu, Eric Hsueh-Chan Chang, Kuei-Hua Ciou, Jing-Mei Natl Cheng Kung Univ Dept Geomat 1 Univ Rd Tainan 701 Taiwan

ISBN: (纸本)9783030419646;9783030419639

In the past, indoor positioning technology was mainly based on pedestrian dead reckoning and wireless signal positioning methods, but it was easy to cause some problems such as error accumulation and signal interference. Positioning accuracy still needs to be improved. With the development of neural networks in recent years, many researchers have successfully applied the neural network to the indoor positioning problem based on the Convolutional Neural Network (CNN). This technique mainly determines the position of the image by matching the image features. CNN faces the same challenges as other supervised learning. If the "clean" data cannot be collected, the trained model will not achieve good positioning accuracy. For CNN used for indoor positioning, if someone passes through in the training data, causing the person to appear in different positions of the images, the model may think that the images are the same location. To solve this problem, we propose a data pre-processing method to improve the accuracy of indoor positioning based on CNN. In this method, the moving objects recognized in training and testing data are modified in different ways. We perform data pre-processing method based onMask R-CNN and YOLO, and then integrate the pre-processing method to PoseNet the famous CNN indoor positioning architecture. Through real experimental analysis, removingmoving objects can effectively improve indoor positioning accuracy about 46%.

关键词： Convolutional Neural Network Indoor positioning Object detection data pre-processing

来源：评论

学校读者我要写书评

暂无评论

data pre-processing Method for Industrie 4.0 Applications 3

Data Pre-Processing Method for Industrie 4.0 Applications

引用

3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Czwick, Cordula Anderl, Reiner Tech Univ Darmstadt Dept Comp Integrated Design Otto Berndt Str 2 D-64287 Darmstadt Germany

The approaches and visions of Industrie 4.0 developed in recent years enable an increase in productivity and efficiency within production while simultaneously increasing flexibility and autonomy. Decisions are made and processes optimized on the basis of collected data from machines and components. To fully exploit the potential of Industrie 4.0, a suitable data basis is necessary. There are limitations regarding the availability and quality of data, but also an oversupply of data without potential to gain additional information is a challenge. Within this paper, a method for data pre-processing in Industrie 4.0 is therefore developed, which aims at increasing the quality of the data and a subsequent successful modelling depending on the underlying use case. To validate the method, three representative use cases are explained and the method for data pre-processing is applied based on these use cases. The paper ends with a discussion of the results. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： data pre-processing data quality Industrie 4.0 use cases

来源：评论

学校读者我要写书评

暂无评论

data pre-processing and data Generation in the Student Flow Case Study 21st

Data Pre-processing and Data Generation in the Student Flow ...

引用

21st International Conference on Intelligent data Engineering and Automated Learning

作者： Cavique, Luis Pombinho, Paulo Tallon-Ballesteros, Antonio J. Correia, Luis Univ Aberta Lisbon Portugal FCUL MAS BioISI Lisbon Portugal Univ Huelva Huelva Spain

ISBN: (纸本)9783030623647;9783030623654

Education covers a range of sectors from kindergarten to higher education. In the education system, each grade has three possible outcomes: dropout, retention and pass to the next grade. In this work, we study the data from the Department of Statistics of Education and Science (DGEEC) of the Education Ministry. DGEEC maintains those outcomes for each school year, therefore, this study seeks a longitudinal view based on student flow. The document reports the data pre-processing, a stochastic model based on the pre-processed data and a data generation process that uses the previous model.

关键词： data pre-processing data generation Student flow Stochastic model

来源：评论

学校读者我要写书评

暂无评论

data pre-processing Techniques in the Regional Emission's Load Profiles Case 6

Data Pre-processing Techniques in the Regional Emission's Lo...

引用

6th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Kewo, Angreine Manembu, Pinrolinvic Nielsen, Per Sieverts Tech Univ Denmark Management Engn Lyngby Denmark Sam Ratulangi Univ Informat Engn Manado Indonesia

ISBN: (纸本)9781728105215

Different data sources, data types and platforms are involved in modelling emissions load profiles. In our case, we model emission load profiles at the regional or city level. However, we found missing values, redundancy and inconsistency in the datasets, and in most cases data pre-processing is unavoidable. data pre-processing converts the data into a clean and tidy dataset for the subsequent modelling steps or statistical analyses. Therefore, some common techniques for data pre-processing such as cleaning, transformation, integration, reduction and some terms in data mining such as filtering and selection have been applied in our case study. We usually do the data pre-processing of moderate problems and a small amount of data using a spreadsheet application, whereas we use the programming language to do the more complex and big data size tasks. As a result, it has been found that understanding the nature of our data collection, the data flow process and the desired output comprehensively is the key for efficiency in data pre-processing. The applied techniques have helped us to provide the proper input for modelling the regional emission load profile efficiently.

关键词： data pre-processing load profile emissions cleaning transformation integration reduction selection modelling

来源：评论

学校读者我要写书评

暂无评论

data pre-processing of Inertial Measurement Unit Based on Abnormity Analysis 27

Data Pre-Processing of Inertial Measurement Unit Based on Ab...

引用

27th Chinese Control and Decision Conference (CCDC)

作者： Fan, Jinhua Song, Jianying Peng, Jie Guo, Xianfeng Taiyuan Satellite Launch Ctr Taiyuan 030027 Peoples R China

ISBN: (纸本)9781479970162

To obtain reasonably the apparent acceleration and angular velocity values from the inertial measurement unit (IMU) telemetry data, a data pre-processing framework is proposed based on abnormity analysis. By introducing some concepts such as continuous/noncontinuous time point (CTP/NCTP), local maximum impulse number, and so on, the abnormity analysis can be implemented according to the characteristics of the IMU output data. Based on the abnormal data positioning, results from both the time point sequence and the impulse number sequences, corresponding measures can be taken including the time outlier correction, abnormal parameters correction at CTP, parameters interpolation at NCTP. The IMU telemetry data generated from a missile flight test are used for demonstration, and the effectiveness of the proposed approach is validated.

关键词： data pre-processing Inertial Measurement Unit Abnormity Analysis

来源：评论

学校读者我要写书评

暂无评论

Effect of data pre-processing on the Performance of Neural Networks for 1-D Transient Electromagnetic Forward Modeling

引用

IEEE ACCESS 2021年 9卷 34635-34646页

作者： Asif, Muhammad Rizwan Bording, Thue S. Barfod, Adrian S. Grombacher, Denys J. Maurya, Pradip K. Christiansen, Anders V. Auken, Esben Larsen, Jakob J. Aarhus Univ Dept Geosci HydroGeophys Grp HGG DK-8000 Aarhus Denmark Aarhus Univ Dept Elect & Comp Engn DK-8200 Aarhus Denmark Geol Survey Denmark & Greenland GEUS DK-1350 Copenhagen Denmark

Geophysical modelling and data inversion are important tools for interpreting the physical properties of Earth's subsurface. Solving the inverse problem involves several computational steps and is generally a time consuming task. Artificial neural networks have the potential to speed up large computations. Such networks provide the means to model the relationship between the inputs and outputs without needing to know the physical model of the underlying problem. There are two main aspects that affect the performance of neural networks: optimization of network architecture and pre-processing of data. In this article, we investigate several traditional pre-processing techniques including the min-max scaling, z-score scaling, and the logarithmic transform scaling, and propose some novel data pre-processing approaches for the 1-D forward modelling of time-domain electromagnetic data based on signal characteristics. We evaluate the performance of the conventional and the proposed pre-processing methods against a 3% relative error metric, which corresponds to the typical data uncertainty, to show that forward data pre-processing has significant effect on the performance of neural networks. The proposed gate-wise min-max scaling achieves the best performance with 96% of gates within a 3% relative error, while the commonly used logarithmic transform results only in 75% of gates within a 3% relative error. We provide insights into how various pre-processing methods affect the performance of these networks and recommend optimal pre-processing strategies that may be used where similar data content is encountered to achieve superior performance. Finally, we show the effect of forward modelling accuracy in inverse modelling.

关键词： data models Computational modeling Neural networks Conductivity Inverse problems Logic gates Numerical models data normalization data pre-processing forward modelling inverse modelling neural networks

来源：评论

学校读者我要写书评

暂无评论

A scalable and effective rough set theory-based approach for big data pre-processing

引用

KNOWLEDGE AND INFORMATION SYSTEMS 2020年第8期62卷 3321-3386页

作者： Chelly Dagdia, Zaineb Zarges, Christine Beck, Gael Lebbah, Mustapha Univ Lorraine CNRS INRIA LORIA Nancy 54000 France Aberystwyth Univ Dept Comp Sci Aberystwyth Dyfed Wales Inst Super Gest Tunis LARODEC Tunis Tunisia Univ Paris North 13 Comp Sci Lab LIPN Villetaneuse France

A big challenge in the knowledge discovery process is to perform data pre-processing, specifically feature selection, on a large amount of data and high dimensional attribute set. A variety of techniques have been proposed in the literature to deal with this challenge with different degrees of success as most of these techniques need further information about the given input data for thresholding, need to specify noise levels or use some feature ranking procedures. To overcome these limitations, rough set theory (RST) can be used to discover the dependency within the data and reduce the number of attributes enclosed in an input data set while using the data alone and requiring no supplementary information. However, when it comes to massive data sets, RST reaches its limits as it is highly computationally expensive. In this paper, we propose a scalable and effective rough set theory-based approach for large-scale data pre-processing, specifically for feature selection, under the Spark framework. In our detailed experiments, data sets with up to 10,000 attributes have been considered, revealing that our proposed solution achieves a good speedup and performs its feature selection task well without sacrificing performance. Thus, making it relevant to big data.

关键词： Big data data pre-processing Rough set theory Distributed processing Scalability High-performance computing

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：