检索结果-内蒙古大学图书馆

1st international Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Wang, Yunpeng Li, Yuzhou Tian, Daxin Wang, Congyu Wang, Wenyang Hui, Rong Guo, Peng Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Intrusion Detection System is a pattern recognition task whose aim is to detect and report the occurrence of abnormal or unknown network behaviors in a given network system being monitored. In this paper, we propose a machine learning model, advanced Naive Bayesian Classification (NBC-A) which is based on NBC and ReliefF algorithm, to be used in the novel IDS. We use ReliefF algorithm to give every attribute of network behavior in KDD’99 dataset a weight that reflects the relationship between attributes and final class for better classification results. The novel IDS has a higher True Positive (TP) rate and a lower False Positive (FP) rate in detection performance. © 2018, ICst Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

An Efficient pattern recognition Based Method for Drug-Drug Interaction Diagnosis

An Efficient Pattern Recognition Based Method for Drug-Drug ...

引用

Artificial Intelligence and data Analytics (CAIDA), international Conference on

作者： Rabia Javed Tanzila Saba Salman Humdullah Nor Shahida Mohd Jamail Mazhar Javed Awan Lahore College for Women University Lahore Pakistan Computer and Information Science Prince Sultan University Riyadh Saudi Arabia Faculty of Computing Universiti Teknologi Malaysia Johor Bahru University of Management and Technology Lahore Pakistan

ISBN: (纸本)9781665430418

The diagnosis of interactions between two drugs is an essential procedure in drug development. Many medical tool's offer inclusive records related to DDI. However, this tool's results are not very satisfactory. The main aim is to propose an efficient approach based on pattern matching that identifies the interaction between two drugs. In this study, the goal is to collect the data from the DrugBank, which is a publicly available source. The drug-related data includes drug ID, drug names, and various kinds of sentences of drug-drug interaction. Drug names will be identified by drug names dictionary defined in the corpus, and sentences will be determined according to given patterns. These sentences will treat as input data, and preprocessing steps will perform in these sentences. Various types of features are selected for machine learning classification. Then all the attributes will be classified into desired classes. The proposed method gains 95.4% accuracy from the random forest classifier.

关键词： Drugs Dictionaries data analysis data preprocessing Random forests pattern matching Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Optimal Feature Subset Selection For pattern Classification and recognition Using Spectral Entropy as a Complexity Measure From Alcohol and Sleep EEG data

Optimal Feature Subset Selection For Pattern Classification ...

引用

Innovations in High Speed Communication and Signal Processing (IHCSP), international Conference on

作者： Takveer Singh Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The quantity of unprocessed data in multi-channel EEG analysis is enormous. Most datasets have some level of noise or redundancy, which could lower the accuracy of any relevant features that are uncovered. This result in a rise in computational complexity and a corresponding rise in the importance of concerns related to memory consumption. Consequently, feature selection has become the matter of significant research in the arenas of machine learning and statistics since it is crucial for efficient analysis of data while needing less memory and time. Based on the findings of this study, selecting features with high class separability from multichannel EEG recordings is an efficient method for performing supervised learning tasks. With this study, we intend to test the following theory: The best feature subset will have features that are substantially linked with the class labels but not connected with each other. Using the same alcoholic EEG database as these other investigations, we created and compared three feature selection algorithms. The purpose here is to use class knowledge in a feature selection technique to enhance the accuracy with which classes may be detected.

关键词： Technological innovation databases Sleep Memory management Supervised learning Signal processing algorithms Sensitivity and specificity

来源：评论

学校读者我要写书评

暂无评论

Depression Detection and recognition Research Based on Audio Analysis and Artificial Intelligence Algorithms

Depression Detection and Recognition Research Based on Audio...

引用

Future Intelligent Technologies for Young Researchers (FITYR), IEEE international workshop on

作者： Zhuozheng Wang Yunlong Wang Xixi Zhao Bingxu Chen Haonan Chen Gang Wang Lei Feng Faculty of Information Technology Beijing University of Technology Beijing China National Clinical Research Center for Mental Disorders & National Center for Mental Disorders Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China National Clinical Research Center for Mental Disorders & National Center for Mental Disorders Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Beijing China

ISBN: (数字)9798331539757

ISBN: (纸本)9798331539764

In response to the prevalent symptoms of depression, the complexity of diagnostic processes, and individuals’ resistance to psychological testing in contemporary society, this study proposes an automated depression detection method based on audio analysis. This method aims to enhance the accuracy of depression state recognition by comparing the efficiency of different models, including machine learning and deep learning. Initially, the study collected audio sample data from 50 individuals, including both healthy subjects and patients with depression. During the audio data processing phase, features related to depression recognition, such as Mel-frequency cepstral coefficients (MFCC) and fundamental frequency, were extracted from these samples. Subsequently, a series of recognition comparison experiments were conducted based on the extracted features, involving various algorithmic models such as Support Vector machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN). Experimental results demonstrated that the Convolutional Neural Network exhibited higher accuracy in recognizing depression states from audio data, with an average recognition accuracy rate of 95.82%. This finding indicates that deep learning models, especially Convolutional Neural Networks, have significant advantages in addressing such issues, providing robust technical support for the future automatic detection of depression.

关键词： Support vector machines Deep learning Resistance Radio frequency Accuracy Depression Feature extraction Convolutional neural networks Random forests Testing

来源：评论

学校读者我要写书评

暂无评论

Remote Sensing Image Object recognition Based on Convolutional Neural Network 1

Remote Sensing Image Object Recognition Based on Convolution...

引用

1st international Conference on Electronics Instrumentation & Information Systems (EIIS)

作者： Zhen, Yumei Liu, Huanyu Li, Junbao Hu, Cong Pan, Jeng-Shyang Harbin Inst Technol Dept Automat Test & Control Harbin 150080 Heilongjiang Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350108 Fujian Peoples R China

ISBN: (纸本)9781538608432

The development of space remote sensing technology brings a lot of remote sensing image data. The traditional target detection method is difficult to adapt to the large amount of high-resolution remote sensing image data. It is necessary to find a way to automatically learn the most effective features from the image data, and to fully recover the correlation between the data. Based on the recognition of the typical targets in remote sensing image data, this paper proposes a method of remote sensing target recognition based on deep learning. In this paper, the pre-train method is introduced to improve the simulation of the model. The experiment of the test set proves the validity of the method.

关键词： deep learning object recognition pre-train

来源：评论

学校读者我要写书评

暂无评论

Scene Text recognition Algorithm Based on Faster RCNN 1

Scene Text Recognition Algorithm Based on Faster RCNN

引用

1st international Conference on Electronics Instrumentation & Information Systems (EIIS)

作者： Wang, Boya Xu, Jianqing Li, Junbao Hu, Cong Pan, Jeng-Shyang Harbin Inst Technol Dept Automat Test & Control Harbin Heilongjiang Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350108 Fujian Peoples R China

ISBN: (纸本)9781538608432

Industrial session of the natural scene in the text recognition technology has a great demand. The traditional optical character recognition technology (OCR) requires the text neat layout and neatness and background clean, and industrial production often fail to meet such standards. In this paper, a new text recognition algorithm based on deep learning is proposed for the existing problems of OCR technology. In this paper, a new method based on convolution neural network (Faster RCNN) is proposed to improve the correctness of text recognition. Compared with the conventional detection method, the correct rate of recognition based on Faster RCNN model can reach 90.4%, and the correctness rate is 88.9%. Experiments show that the recognition method in this paper is effective.

关键词： deep learning scene text recognition convolution neural network

来源：评论

学校读者我要写书评

暂无评论

Leveraging Semi-Supervised learning to Enhance data mining for Image Classification under Limited Labeled data

Leveraging Semi-Supervised Learning to Enhance Data Mining f...

引用

Electronic Information Engineering and Computer Communication (EIECC), international Conference on

作者： Aoran Shen Minghao Dai Jiacheng Hu Yingbin Liang Shiru Wang Junliang Du University of Michigan Ann Arbor USA Columbia University New York USA Tulane University New Orleans USA Northeastern University Seattle USA Dartmouth College Hanover USA Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. This study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. The experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios.

关键词： Support vector machines Visualization Accuracy Semisupervised learning Feature extraction Classification algorithms Behavioral sciences data mining Convolutional neural networks Image classification

来源：评论

学校读者我要写书评

暂无评论

Towards privacy-preserving model selection

Lecture Notes in Computer Science (including subseries Lectu...

引用

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2008年 4890 LNCS卷 138-152页

作者： Yang, Zhiqiang Zhong, Sheng Wright, Rebecca N. Department of Computer Science Stevens Institute of Technology Hoboken NJ 07030 United States Department of Computer Science SUNY Buffalo Buffalo NY 14260 United States Department of Computer Science DIMACS Rutgers University Piscataway NJ 08854 United States

ISBN: (纸本)3540784772

Model selection is an important problem in statistics, machine learning, and data mining. In this paper, we investigate the problem of enabling multiple parties to perform model selection on their distributed data in a privacy-preserving fashion without revealing their data to each other. We specifically study cross validation, a standard method of model selection, in the setting in which two parties hold a vertically partitioned database. For a specific kind of vertical partitioning, we show how the participants can carry out privacy-preserving cross validation in order to select among a number of candidate models without revealing their data to each other. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： data privacy

来源：评论

学校读者我要写书评

暂无评论

An Efficient Predictive Analysis Model of Customer Purchase Behavior using Random Forest and XGBoost Algorithm

An Efficient Predictive Analysis Model of Customer Purchase ...

引用

Convergence in Engineering (ICCE), IEEE international Conference for

作者： Subhatav Dhali Monalisha Pati Soumi Ghosh Chandan Banerjee Netaji Subhash Engineering College Kolkata India

ISBN: (数字)9781728173405

ISBN: (纸本)9781728173412

Predictive Analytics is a bough of the advanced analytics which has been used to make predictions about unknown future events. It uses many techniques from data mining and statistics Modelling to analyze the current data. In statistics Modeling, Regression Analysis algorithms are some of the most popular processes used in machine learning Models. Random Forest is a supervised learning algorithm which uses Ensemble learning method to take advantage of Bootstrap Aggregating. XGBoost is a scalable & accurate implementation of Gradient Boosting machines (GBMs). It has been proved to push the limits of computing power. It is built & developed for the sole purpose of model performance and computational speed. Customers are the basis for growth of any type of business. In the study of sales and purchase, it is vital & crucial to be able to predict the amount of purchase or sales to increase benefit by catering from specific products to specific demographics. Our prediction analysis model can effectively help to improve the performance and increase the profit margin. Moreover, it can generalize the prediction of purchase or sales figures in any market which depends on the customers' past purchase pattern or behavior.

关键词： Predictive models Prediction algorithms machine learning algorithms Random forests Analytical models Training data models

来源：评论

学校读者我要写书评

暂无评论

Accidental Face recognition and Detection Using machine learning

Accidental Face Recognition and Detection Using Machine Lear...

引用

Advanced Innovations in Smart Cities (ICAISC), international Conference on

作者： Ashish Sharma Piyansh Agrawal Krishan Bhisham Sharma Imed Ben Dhaou Department of Computer Engineering & Applications GLA University Mathura UP India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

The data collected from all the states and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms.

关键词： Resistance Technological innovation Road accidents Social networking (online) Smart cities Face recognition Roads

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：