检索结果-内蒙古大学图书馆

2014 2nd International Conference on Economic,Business Management and Education Innovation(EBMEI 2014)

作者： Xu Jia Yun Bai School of Science HeBei University of Technology School of Economics and Management HeBei University of Technology

With the occurrence of violence in Kun Ming railway stations, policing monitoring system in security issues has become the focus of attention. The article analyzes the application of association rules and classification algorithm in the policing monitoring system, instructing feasibility and convenience of this idea from the perspective of data mining technology.

关键词： Data Mining Policing Monitoring System Association Rules classification algorithm

来源：评论

学校读者我要写书评

暂无评论

Affective-feature-based Sentiment Analysis using SVM Classifier

Affective-feature-based Sentiment Analysis using SVM Classif...

引用

IEEE International Conference on Computer Supported Cooperative Work in Design

作者： Fang Luo Cheng Li Zehui Cao School of Aerospace Tsinghua University Department of Computer Science and Technology Wuhan University of Technology

ISBN: (纸本)9781509019168

Based on the methods of the traditional topic-based text classification, machine learning method was performed to the coarse-grained sentiment classification of reviews. Sentiment classification involved a lot of problems. In this paper, the sentiment Vector Space Model (s-VSM) was used for text representation to solve data sparseness. In addition, the critical issues of the sentiment classification, i.e. the selection of classification algorithms, the determination of feature selection method and the selection of feature dimension, are verified by experiments. Furthermore, in order to consider the entire corpus contribution of features and each category contribution of features, the feature selection method of Chi-square Difference between the Positive and Negative Categories (CDPNC) was proposed. It combined DF with CHI and had the better performance. Experiments showed that the Macro-F and Micro-F achieved 90.18% and 90.08% respectively.

关键词： Reviews Sentiment classification Machine learning classification algorithm Feature selection

来源：评论

学校读者我要写书评

暂无评论

Knowledge discovery through SysFor: a systematically developed forest of multiple decision trees 11

Knowledge discovery through SysFor: a systematically develop...

引用

Proceedings of the Ninth Australasian Data Mining Conference - Volume 121

作者： Zahidul Islam Helen Giggins Charles Sturt University NSW Australia Newcastle University Callaghan Australia

ISBN: (纸本)9781921770029

Decision tree based classification algorithms like C4.5 and Explore build a single tree from a data set. The two main purposes of building a decision tree are to extract various patterns/logic-rules existing in a data set, and to predict the class attribute value of an unlabeled record. Sometimes a set of decision trees, rather than just a single tree, is also generated from a data set. A set of multiple trees, when used wisely, typically have better prediction accuracy on unlabeled records. Existing multiple tree techniques are catered for high dimensional data sets and therefore unable to build many trees from low dimensional data sets. In this paper we present a novel technique called Sys-For that can build many trees even from a low dimensional data set. Another strength of the technique is that instead of building multiple trees using any attribute (good or bad) it uses only those attributes that have high classification capabilities. We also present two novel voting techniques in order to predict the class value of an unlabeled record through the collective use of multiple trees. Experimental results demonstrate that SysFor is suitable for multiple pattern extraction and knowledge discovery from both low dimensional and high dimensional data sets by building a number of good quality decision trees. Moreover, it also has prediction accuracy higher than the accuracy of several existing techniques that have previously been shown as having high performance.

关键词： classification algorithm data mining prediction accuracy multiple decision tree

来源：评论

学校读者我要写书评

暂无评论

Research on Customer Deposit Order Based on Data Mining Technology 2019

Research on Customer Deposit Order Based on Data Mining Tech...

引用

Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City

作者： Aiting Xu Yingying Zhou Hui Chen College of Statistics and Mathematics Zhejiang Gongshang University Hangzhou China

ISBN: (纸本)9781450376631

This paper takes the time deposit order data of banking institutions as the research object. Firstly, the data of banking institutions are analyzed and cleaned to convert the categorical data into numeric data. Then, to "split" preprocessing the data after processing, divided into training set and test set. In the training set, LR, Naive Bayes, K-NN, Decision Tree, Random Forests, Extra - trees, AdaBoost, GBDT, XGBoost, LightGBM and CatBoost algorithm (via the grid search method GridSearchCV) are established to study the customer's deposit ordering tendency respectively. Finally, the Stacking fusion algorithm in Ensemble Learning(The meta-learner adopts LR algorithm) is used to fuse each single model to build the best bank customer time deposit ordering classification model. The research results show that using python, based on the different dimensions of bank customer information, the Stacking fusion algorithm based on Ensemble algorithm has better prediction effect than the single model, and the Stacking fusion algorithm has better robustness. Therefore, this paper takes the Stacking fusion model as the final prediction model to effectively identify bank customers and help banking institutions understand customer subscription tendency.

关键词： Customer deposit ordering Stacking fusion algorithm classification algorithm Machine learning

来源：评论

学校读者我要写书评

暂无评论

DeepDBP: Deep neural networks for identification of DNA-binding proteins

引用

Informatics in Medicine Unlocked 2020年 19卷

作者： Shadab, Shadman Alam Khan, Md Tawab Neezi, Nazia Afrin Adilina, Sheikh Shatabda, Swakkhar Department of Computer Science and Engineering United International University Plot-2 United City Madani Avenue Badda Dhaka 1212 Bangladesh

DNA-Binding proteins (DBP) are associated with many cellular level functions which includes but not limited to body's defense mechanism and oxygen transportation. They bind DNAs and interact with them. In the past DBPs were identified using experimental lab based methods. However, in the recent years researchers are using supervised learning to identify DBPs solely from protein sequences. In this paper, we apply deep learning methods to identify DBPs. We have proposed two different deep learning based methods for identifying DBPs: DeepDBP-ANN and DeepDBP-CNN. DeepDBP-ANN uses a generated set of features trained on traditional neural network and DeepDBP-CNN uses a pre-learned embedding and Convolutional Neural Network. Both of our proposed methods were able to produce state-of-the-art results when tested on standard benchmark datasets. DeepDBP-ANN had a train accuracy of 99.02% and test accuracy of 82.80%.And DeepDBP-CNN though had train accuracy of 94.32%, it excelled at identifying test instances with 84.31% accuracy. All methods are available codes and methods are available for use at: https://***/antorkhan/DNABinding. © 2020 The Authors

关键词： classification algorithm CNN Deep learning DNA-Binding proteins Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Mangrove monitoring revealed by MDPrePost-Net using archived Landsat imageries

引用

REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 2023年 32卷

作者： Dimyati, Muhammad Umarhadi, Deha Agus Jamaluddin, Ilham Awanda, Disyacitta Widyatmanti, Wirastuti Univ Indonesia Fac Math & Nat Sci Dept Geog Depok 16424 Indonesia Univ Gadjah Mada Fac Forestry Dept Forest Management Yogyakarta 55281 Indonesia Natl Cent Univ Dept Comp Sci & Informat Engn 300 Jhongda Rd Jhongli 32001 Taoyuan Taiwan Univ Gadjah Mada Fac Geog Dept Geog Informat Sci Bulaksumur 55281 Yogyakarta Indonesia

Acting as a global vital entity in the coastal ecosystem, mangroves are currently facing major threats to destruction due to anthropogenic activities. Restoration and rehabilitation measures of mangroves are being carried out, including in Indonesia. To support conservation actions in many islands in Indonesia, spatio-temporal information on mangroves is required. Remote sensing with advanced techniques such as machine learning and deep learning has proven the capability to spatially observe mangroves. This study aims to monitor mangrove change in three study areas, i.e., South Sumatra, North Kalimantan, and Southeast Sulawesi, using a fully convolutional network (FCN)-based MDPrePost-Net. This method was developed originally to assess the man-grove degradation due to a major event (i.e., Hurricane irma 2017 in southwest Florida), whereas this study adopts it for an extended observation period (1989-2022 for South Sumatra, 1991-2021 for North Kalimantan, and 1990-2021 for Southeast Sulawesi) using medium-resolution Landsat imageries. We observed that the mangroves remain stable within the national parks designated by the government. Outside the national parks, mangrove conversion massively occurred even though the areas are assigned as protection forests. The classification results showed satisfactory accuracy values of more than 84%. The maps produced have an advantage in the spatial change analysis compared to the global datasets such as Global Mangrove Watch version 3.0. Our method has a limitation when cloudless images are not available. The integration with Synthetic Aperture Radar (SAR) images and a rigorous cloud removal method for the optical images may improve the results of mangrove monitoring.

关键词： Deep learning Fully convolutional network Remote sensing classification algorithm Wetland

来源：评论

学校读者我要写书评

暂无评论

Assessment of machine learning classifiers in mapping the cocoa-forest mosaic landscape of Ghana

引用

SCIENTIFIC AFRICAN 2023年 20卷

作者： Ashiagbor, George Asare-Ansah, Akua Oparebea Amoah, Emmanuel Boakye Asante, Winston Adams Mensah, Yaw Asare Kwame Nkrumah Univ Sci & Technol KNUST Fac Renewable Nat Resources FRNR Kumasi Ghana Kwame Nkrumah Univ Sci & Technol KNUST Dept Geomat Engn Kumasi Ghana

The absence of clear-cut directives on the optimal classifier for land-use land-cover (LULC) classification of Ghana's cocoa landscape presents a practice gap. This has resulted in monitoring challenges since it is difficult to effectively compare land cover maps because they differ in the classifiers. In this paper, we explored the performance of four commonly used machine learning classifiers in the cocoa landscape to accurately determine the option that best segregates the different vegetation classes. Specifically, the accuracy with which k-Nearest Neighbors (kNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF) classifiers mapped the cocoa landscape in Juaboso, Ghana, was compared. A pre-processed Sentinel-2 image, 352 training and 151 validation points collected through field Global Positioning System survey were used. The image was classified using the kNN, ANN, SVM and RF classifiers. The accuracies of the LULC maps were assessed using overall accuracy (OA), Cohens' kappa (k). Also, practitioners with practical knowledge of the land cover classes and their distribution in the landscape subjected the map to visual inspection. The OA and k values indicated RF (OA = 84.77%, k = 0.801), kNN (OA = 84.11%, k = 0.796), ANN (OA = 76.13%, k = 0.7), and SVM (OA = 81.45%, k = 0.762) all performed well in classifying the landscape with a satisfactory agreement. Additionally, there are no clear-cut classifier that experts in remote sensing should apply while mapping Ghana's cocoa environment. At any point, using the classifier that most accurately represents the landscape is crucial and should be prioritized. Therefore, guidance on the choice of classification algorithms by researchers and practitioners for mapping the cocoa landscape of Ghana must not be limited to the overall accuracies and kappa only. Instead, operationalising a mapping and validation framework that incorporates experts' review will yield a LULC map that better

关键词： Remote sensing classification algorithm High forest zone of Ghana Cocoa deforestation Expert validation

来源：评论

学校读者我要写书评

暂无评论

NETWORK INTRUSION DETECTION USING CLUSTERING AND GRADIENT BOOSTING

NETWORK INTRUSION DETECTION USING CLUSTERING AND GRADIENT BO...

引用

International Conference on Computing, Communication and Networking Technologies

作者： Parag Verma Shadab Khan Shayan Anwar Sunil B. Mane Department of Computer Engineering & IT College of Engineering Pune

An unauthorized activity on the network is called network intrusion and device or software application which monitors the network parameters in order to detect such an intrusion is called network intrusion detection system (NIDS). With high rise in malicious activities on the internet, it is extremely important for NIDS to quickly and correctly identify any kind of malicious activity on the network. Moreover, the system must refrain from raising false alarms in case of normal usage detected as malicious. This paper proposes use of machine learning classification algorithms - XGBoost and AdaBoost with and without clustering to train a model for NIDS. The models are trained and tested using NSL KDD dataset and the results are an improvement over the previous works related to intrusion detection on the same dataset.

关键词： Intrusion detection XGBoost AdaBoost Machine learning Decision trees classification algorithm Clustering algorithm Gradient boosting

来源：评论

学校读者我要写书评

暂无评论

Study and implementation of automated system for detection of PCOS from ultrasound scan images using artificial intelligence

引用

The Journal of Photographic Science 2024年第7期72卷

作者： M. Sumathi P. Chitra S. Sheela C. Ishwarya Sathyabama Institute of Science and Technology Chennai India

ABSTRACTArtificial Intelligence (AI), is a field of science and engineering that deals with intelligent behaviour which has the potential of improved access and the cost of healthcare *** Ovarian Syndrome (PCOS) is characterised by a protracted menstrual cycle and frequent excess androgen levels, which often affect many women of reproductive age. There are now no trustworthy objective tests that might fully confirm the diagnosis and comprehension of PCOS. Effective image processing steps have been utilized in this work for the automation of PCOS diagnosis and the classification algorithms used are DarkNet-19, AlexNet, SqueezeNet, and SVM. As a result of the classifier's accelerated PCOS diagnosis and improved performance analysis, there will be fewer instances of potentially deadly consequences that can arise from a delayed diagnosis. The system produced metrics demonstrating improved performance, such as accuracy to provide a comprehensive ultrasound picture diagnosis using the classifier DarkNet-19 with 99%.

关键词： AI classification algorithm detection of PCOS ovary of ultrasound scan images performance metrics segmentation feature extraction graphical user interface

来源：评论

学校读者我要写书评

暂无评论

OCR for devanagari numerals using zonal histogram of angle

引用

JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS 2017年第4期20卷 519-534页

作者： Johnson, Kent Gourav, Kumar Gaurav Rudrapal, Dwijen Debnath, Sanjib Natl Inst Technol Agartala Dept Comp Sci & Engn Jirania 799046 Tripura India

Optical Character Recognition (OCR) system is used to generate the textual representation of handwritten or printed text. Many research works are going on in the field of OCR over the past few decades for most of the Indian scripts. Devanagari one of the most spoken languages in the world as well as India. Lack of a robust OCR system for Devanagari script is still there even after so much research. The aim of this paper is to make an OCR that could classify handwritten Devanagari numerals. This paper proposes an OCR based on Histogram of the angle made by a dark pixel with the zonal center of mass. This feature bags the angle made by each dark pixel in a zone about its center of mass. This newly extracted feature was used to train various classification algorithms like K-Nearest Neighbor, SVM, Linear SVM, Random Forest, Decision Tree, Gradient Boosting, Gaussian Naive Bayes. We reported an efficiency of each algorithm based on the new feature. Our experiment result shows that the Random Forest Model outperforms over the other algorithms and reports an efficiency of 92.57%.

关键词： Devanagari Numeral OCR Angular Histogram Machine learning algorithms Feature extraction classification 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：