检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

749 篇 会议
8 册 图书
6 篇 期刊文献

馆藏范围

763 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

465 篇 工学
- 384 篇 计算机科学与技术...
- 289 篇 软件工程
- 169 篇 信息与通信工程
- 92 篇 电气工程
- 84 篇 生物工程
- 75 篇 控制科学与工程
- 71 篇 生物医学工程（可授...
- 46 篇 网络空间安全
- 38 篇 安全科学与工程
- 37 篇 交通运输工程
- 36 篇 电子科学与技术（可...
- 29 篇 仪器科学与技术
- 26 篇 机械工程
- 24 篇 光学工程
- 21 篇 化学工程与技术
204 篇 理学
- 98 篇 数学
- 91 篇 生物学
- 46 篇 统计学（可授理学、...
- 43 篇 物理学
- 39 篇 系统科学
- 29 篇 化学
132 篇 管理学
- 82 篇 图书情报与档案管...
- 74 篇 管理科学与工程(可...
- 26 篇 工商管理
67 篇 医学
- 57 篇 临床医学
- 46 篇 基础医学(可授医学...
- 39 篇 药学(可授医学、理...
22 篇 法学
- 21 篇 社会学
16 篇 农学
- 16 篇 作物学
15 篇 教育学
- 14 篇 教育学
9 篇 经济学
5 篇 军事学
1 篇 文学

主题

205 篇 machine learning
60 篇 signal processin...
47 篇 feature extracti...
45 篇 deep learning
45 篇 signal processin...
39 篇 support vector m...
36 篇 machine learning...
31 篇 training
27 篇 accuracy
22 篇 computational mo...
18 篇 neural networks
16 篇 data models
15 篇 predictive model...
15 篇 image processing
14 篇 natural language...
14 篇 forecasting
13 篇 electroencephalo...
13 篇 real-time system...
13 篇 classification
13 篇 artificial intel...

机构

7 篇 chitkara univers...
4 篇 school of comput...
4 篇 lloyd law colleg...
3 篇 telangana hydera...
3 篇 school of comput...
3 篇 institute of aer...
3 篇 lloyd institute ...
3 篇 school of electr...
3 篇 department of co...
3 篇 school of comput...
3 篇 department of el...
3 篇 department of me...
3 篇 central universi...
3 篇 lloyd law colleg...
3 篇 lloyd institute ...
3 篇 college of compu...
3 篇 school of electr...
3 篇 co. ltd.
2 篇 school of presch...
2 篇 beijing institut...

作者

4 篇 shaik subhani
4 篇 wathiq mansoor
4 篇 mansoor wathiq
3 篇 anand r.
3 篇 subba rao s.p.v.
3 篇 gabbouj moncef
3 篇 yazici ibrahim
3 篇 i̇brahim yazici
3 篇 k mayuri
2 篇 rui wang
2 篇 anas abou el kal...
2 篇 guo jianxin
2 篇 mehdi salkhordeh...
2 篇 a l n rao
2 篇 shuja mirza
2 篇 yatika gori
2 篇 ahmed qtaishat
2 篇 cao jiuwen
2 篇 mirza shuja
2 篇 liu yang

语言

744 篇 英文
15 篇 其他
9 篇 中文

检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 763 条记录，以下是301-310 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

A Comparative Analysis of Different Pre-processing Pipelines for EEG-Based Preference Prediction in Neuromarketing 6

A Comparative Analysis of Different Pre-Processing Pipelines...

引用

6th IEEE Pune Section international conference, PuneCon 2023

作者： Khondakar, Md. Fazlul Karim Trovee, Trishita Ghosh Hasan, Mahmudul Sarowar, Md. Hasib Chowdhury, Mehdi Hasan Hossain, Quazi Delwar Chittagong University of Engineering & Technology Department of Biomedical Engineering Chittagong Bangladesh Chittagong University of Engineering & Technology Department of Electrical &electronic Engineering Chittagong Bangladesh Bangladesh University of Engineering & Technology Department of Electrical &electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350324204

Neuromarketing is an emerging brain-computer interface (BCI) research field that aims to understand consumers' internal decision-making processes when choosing which products to buy. It provides valuable insights for marketers to improve their marketing strategies based on consumers' impressions. However, the current status of Electroencephalography (EEG)-based preference prediction and its classification accuracy is still below optimal. the performance of an EEG-based preference prediction system largely depends on the suitable selection of a proper EEG preprocessing pipeline since noisy EEG data is likely not to give better results. Most of the studies followed a traditional EEG pre-processing pipeline and also recently an advanced automated EEG pre-processing pipeline has been introduced in the literature. In this study, we have proposed an optimal EEG pre-processing pipeline and compared it with the other two methods mentioned above. We collected raw EEG data and pre-processed them using the three pre-processing pipelines. We then extracted many statistical and frequency domain features and employed several machine-learning classification models. After implementing our proposed pipeline to preprocess EEG data, we observed a significant improvement in classification accuracies. Specifically, our pipeline achieved 100% accuracy for Ensemble learning, and 93.3% accuracy for k-Nearest Neighbor (kNN) model. In comparison, for the same classifiers the traditional and advanced automated pipelines achieved 75%, 83.3%, and 78.3%, 87% accuracies respectively. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Mental Workload Classification with One-Dimensional CNN Using fNIRS signal 10th

Mental Workload Classification with One-Dimensional CNN Usin...

引用

10th Biennial international conference on Pattern Recognition and machine Intelligence (PReMI)

作者： Kumar, Ashish Karmakar, Subashis Agarwal, Isha Pal, Tandra Natl Inst Technol Durgapur Dept Comp Sci & Engn Durgapur West Bengal India

ISBN: (纸本)9783031451690;9783031451706

As one of the promising technology, Brain-Computer Interface assists people by exploring various aspects of brain functionality. Analyzing and decoding brain signals to classify mental workload can help in the diagnosis and treatment of various neurodegenerative diseases and neurological disorders like learning disability. In this pursuit, functional near-infrared spectroscopy has emerged as a promising non-invasive technique that uses blood flow patterns to analyze brain signals. In this study open-access functional near-infrared spectroscopy datasets are used for mental workload classification. We have considered it as a two class problem and proposed a one-dimensional convolutional neural network to distinguish between mental task and rest, and compared the performance with two other machine learning methods, support vector machine and deep neural network. the proposed one-dimensional convolutional neural network model eliminates the requirement for complex brain image processing providing a computationally efficient alternative and comparable accuracy.

关键词： Brain-computer interface (BCI) fNIRS Cognitive Load Mental Task Convolutional Neural Network (CNN)

来源：评论

学校读者我要写书评

暂无评论

Aspect-Based Sentiment Classification of Online Product Reviews Using Hybrid Lexicon-machine learning Approach 1

引用

6th international conference on Applied machine learning and Data Analytics, AMLDA 2023

作者： Asuquo, Daniel Attai, Kingsley Usip, Patience George, Uduak Osang, Francis Department of Computer Science Faculty of Science University of Uyo Uyo Nigeria TETFund Center of Excellence in Computational Intelligence Research UNIUYO Uyo Nigeria Department of Mathematics and Computer Science Ritman University IkotEkpene Ikot Ekpene Nigeria Department of Computer Science National Open University of Nigeria Abuja Nigeria

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

Nowadays, a good number of customers express their experience with online products. these reviews have an important role in customers’ purchase decision process. there may be hundreds or thousands of unstructured and heterogeneous reviews for a popular product. Traditional text processing techniques have limited capability in extracting opinions on customers’ product reviews from huge data over the Internet. Although lexical approaches aim to map words to sentiments by building a lexicon, the process of developing a lexicon with sentiment scores for phrases and sentences becomes tedious and time consuming as data volume increases. Currently, text sentiment analysis requires fast and accurate techniques to decode and quantify the emotion in tweets. this paper presents a hybrid framework based on lexicon and machine learning (ML) algorithms to train previously seen tweets in order to predict the sentiments of some new input tweets into positive, negative, and neutral polarities. Tweepy library was used to extract tweets on Laptop reviews to identify some aspects and classify sentiments towards them into specific polarity. After data pre-processing, the implementation in Python used Natural Language processing package called TextBlob to assign subjectivity and polarity scores to text. the scores were used by the ML algorithms to analyze and classify sentiments. A dataset of 2226 tweets was used for training and testing Support Vector machine, Random Forest, and Naïve Bayes classifiers. Results indicate that Random Forest classifier outperforms others in the task of classifying sentiments on Laptop reviews with the highest accuracy (96%), precision (97%), and F1-score (96%). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Two-way Speech to Sign Language Converter application using Python, OpenCV and NLP 6

Two-way Speech to Sign Language Converter application using ...

引用

6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Suman, Shashikant Lohani, Bhanu Prakash Singh, Vijay Gupta, Amar Deep Khan, Akhilesh Kumar Kumar, Anil Amity School of Engineering & Technology Amity University Department of Computer Science Uttar Pradesh Greater Noida India Lloyd Institute of Management and Technology Greater Noida Uttar Pradesh India Lloyd Law College Greater Noida Uttar Pradesh India

ISBN: (纸本)9798350304480

Language is one of the cornerstones of modern society. It has a very important role as it is responsible for conveying and sharing information with others. the people who are challenged with hearing and speaking disabilities are unable to communicate well with others. To aid them in this scenario, we have developed a program which can translate Sign Language to speech and vice versa. the models for the program were created with the help of public ISL datasets containing sign dictionaries. Speech to sign language modules used speech to text and NLP principles to generate signs for the provided sentence. Random Forest classifier was used for sign recognition. © 2023 IEEE.

关键词： CNN communication machine learning Natural Language processing RF Sign language

来源：评论

学校读者我要写书评

暂无评论

Interpretable analysis of feature importance and implicit correlation based on sEMG grayscale images 6

Interpretable analysis of feature importance and implicit co...

引用

IEEE 6th international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Ao, Xiaohu Wang, Feng Zhao, Juan She, Jinhua China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Tokyo Univ Technol Sch Engn Hachioji Tokyo 1920982 Japan

ISBN: (纸本)9798350311259

For patients requiring upper limb rehabilitation, the hand rehabilitation robot assists the patient in completing movements within a certain training trajectory to achieve therapeutic results. there have been studies based on deep learning to convert surface electromyography (sEMG) signals into sEMG images for motion intention analysis. Although good recognition accuracy has been achieved, the working principle of neural networks and the processing of image features by the networks are not well explained. the interpretability of deep neural networks determines human confidence in neural network decisions. In this paper, we design a method based on feature importance and implicit correlation for hand motion intention recognition, experimentally explored that convolutional neural networks have implicit definitions for sEMG grayscale images of the same hand gesture action, and verified the effectiveness of the designed method.

关键词： sEMG signal grayscale images implicit correlation interpretability

来源：评论

学校读者我要写书评

暂无评论

Technical Debt Classification in Issue Trackers using Natural Language processing based on Transformers 6

Technical Debt Classification in Issue Trackers using Natura...

引用

6th ACM/IEEE international conference on Technical Debt (TechDebt)

作者： Skryseth, Daniel Shivashankar, Karthik Pilan, Ildiko Martini, Antonio Univ Oslo Dept Informat N-0316 Oslo Norway Norwegian Comp Ctr SAMBA Oslo Norway

ISBN: (纸本)9798350311945

Background: Technical Debt (TD) needs to be controlled and tracked during software development. Support to automatically track TD in issue trackers is limited. Aim: We explore the usage of a large dataset of developer-labeled TD issues in combination with cutting-edge Natural Language processing (NLP) approaches to automatically classify TD in issue trackers. Method: We mine and analyze more than 160GB of textual data from Github projects, collecting over 55,600 TD issues and consolidating them into a large dataset (GTD dataset). We use such datasets to train and test Transformer ML models. then we test the model's generalization ability by testing them on six unseen projects. Finally, we re-train the models including part of the TD issues from the target project to test their adaptability. Results and conclusion: (i) We create and release the GTD dataset, a comprehensive dataset including TD issues from 6,401 public repositories with various contexts;(ii) By training Transformers using the GTD dataset, we achieve performance metrics that are promising;(iii) Our results are a significant step forward towards supporting the automatic classification of TD in issue trackers, especially when the models are adapted to the context of unseen projects after fine-tuning.

关键词： Technical Debt Natural Language processing machine learning Issue Tracking Data Mining

来源：评论

学校读者我要写书评

暂无评论

A Data-Driven Approach to Forecasting Bangladesh Next-Generation Economy 1

引用

4th international conference on machine Intelligence and signal processing, MISP 2022

作者： Shohug, Md. Mahfuj Hasan Bitto, Abu Kowshir Rubi, Maksuda Akter Bijoy, Md. Hasan Imam Rahaman, Ashikur Department of Software Engineering Daffodil International University Dhaka1216 Bangladesh Department of General Educational Development Daffodil International University Dhaka1216 Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

ISBN: (数字)9789819900473

ISBN: (纸本)9789819900466

Bangladesh is the most populous country on the planet. We recognized Bangladesh as an independent sovereign country, yet despite its large population, Bangladesh lags far behind the world’s wealthiest nations. In the developed world, especially in terms of economics, a country’s Gross Domestic Product (GDP) is the monetary value of all completed labor and products produced within its borders during a period. It is concerned with the overall measurement of all financial transactions. In this research, we use a machine learning time series ARIMA model to correctly estimate the Bangladesh GDP Growth Rate with the order of (0, 1, 1). the auto ARIMA function generates a minimal AIC value, which is used to verify the model. the SARIMAX (0, 1,1, 6) model can predict the GDP with 87.51% accuracy. We implement this machine learning time series ARIMA model on the web application;GDP indicator. Users may find the GDP growth rate for Bangladesh in the future here, as well as watch our country’s upcoming economy. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Memory Improvement System Based on Music-Audio-GAN Music Generation

Memory Improvement System Based on Music-Audio-GAN Music Gen...

引用

IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Siyun Liang International Curriculum Center The High School Affiliated to Renmin University of China Beijing China

Generative Adversarial Network (GAN) is a machine learning technology that can be used to generate various types of data, including music. In this project, a GAN network is used to generate music with rhythm and melody. We want to show that this type of music can improve teenagers' attention and concentration. GAN networks learn the structure and characteristics of music from training sets, and then generate new music. the generated music can be adjusted according to our requirements to meet different needs. this paper proposes a Music-Audio-GAN music generation system, which can generate various types of music. We have tested various types of teenagers using GAN network to generate music, indicating that the generated music can improve their attention and learning abilities.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Using Sentiment Analysis to Spot Trending Products 6

Using Sentiment Analysis to Spot Trending Products

引用

6th international conference on Vocational Education and Electrical Engineering, ICVEE 2023

作者： Darraz, Nossayba Karabila, Ikram El-Ansari, Anas Alami, Nabil Lazaar, Mohamed Mallahi, Mostafa El Sidi Mohamed Ben Abdellah University High Normal School Department of Mathematics and Computer Science IPI Laboratory Fez Morocco Mohammed First University Polydisciplinary Faculty Computer Science Department MASI Laboratory Nador Morocco Mohammed First University Higher School of Technology MASI Laboratory Nador Morocco Mohamed v University ENSIAS Rabat Morocco

ISBN: (纸本)9798350326642

Trend detection refers to the process of recognizing significant patterns or trends within large datasets, including changes over time and seasonal variations. In the context of product-related data, trend product detection is a specialized tool or software that is specifically designed to identify trends in sales, customer preferences, product reviews, and other relevant data points. the study focuses on the combination of trend detection and sentiment analysis as a powerful approach for trend spotting. In this paper, we present a study that uses natural language processing (NLP) techniques and the VADER sentiment analysis tool to identify product sentiment from customer reviews. We then applied decision tree analysis to predict whether a product is likely to trend or not based on its sentiment score. Additionally, we used clustering to group the top-rated products based on their sentiment scores and applied a moving average to determine which products are currently trending. Our results show that the combination of trend detection and sentiment analysis can effectively identify trending products with an accuracy of 93%. this study demonstrates the power of NLP and sentiment analysis in detecting trends and provides valuable insights for businesses to make data-driven decisions about their product offerings. © 2023 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

An Extensive Evaluation of Plant Disease Detection Using Diverse machine learning Approaches

An Extensive Evaluation of Plant Disease Detection Using Div...

引用

international conference on signal processing and Information Security (ICSPIS)

作者： Falguni Suthar Kuldeep Padhya Ritesh Joshi Satyen Parikh Alavikunhu Panthakkan Wathiq Mansoor A.M.Patel Institute of Computer Ganpat University Mehsana India University of Dubai Dubai United Arab Emirates

Plant sicknesses fundamentally affect horticultural efficiency and food security. In order to minimize widespread damage and maximize crop yield, it is essential to detect these diseases promptly and accurately. Automating the process of detecting plant diseases has been shown to be a promising application of machine learning techniques in recent years. this study presents a comprehensive analysis of various machine learning techniques applied to the task of plant disease detection. this study investigates the use of different AI procedures for plant illness recognition. Convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and k-nearest neighbors (KNNs) are just a few of the algorithms that are compared in this study. Feature extraction from diseased plant images is the first step in our experiment, followed by model training and validation. the results demonstrate the efficacy of machine learning in accurately identifying plant diseases based on visual symptoms. the findings provide insights into the strengths and limitations of each technique, paving the way for further research in automated and precise plant disease diagnosis to enhance agricultural sustainability.

关键词：

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共77页 << < 27 28 29 30 31 32 33 34 35 36 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：