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

1,230 篇 会议
37 篇 期刊文献

馆藏范围

1,267 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

654 篇 工学
- 563 篇 计算机科学与技术...
- 457 篇 软件工程
- 237 篇 信息与通信工程
- 136 篇 控制科学与工程
- 99 篇 电气工程
- 94 篇 生物工程
- 90 篇 生物医学工程（可授...
- 79 篇 网络空间安全
- 73 篇 电子科学与技术（可...
- 55 篇 安全科学与工程
- 53 篇 光学工程
- 36 篇 交通运输工程
- 34 篇 仪器科学与技术
- 32 篇 机械工程
- 30 篇 动力工程及工程热...
- 29 篇 化学工程与技术
369 篇 理学
- 227 篇 数学
- 114 篇 统计学（可授理学、...
- 102 篇 生物学
- 54 篇 物理学
- 33 篇 化学
- 29 篇 系统科学
252 篇 管理学
- 177 篇 管理科学与工程(可...
- 106 篇 图书情报与档案管...
- 56 篇 工商管理
85 篇 医学
- 80 篇 基础医学(可授医学...
- 79 篇 临床医学
- 43 篇 公共卫生与预防医...
- 43 篇 药学(可授医学、理...
39 篇 法学
- 36 篇 社会学
26 篇 经济学
21 篇 农学
7 篇 教育学
3 篇 军事学

主题

177 篇 feature extracti...
166 篇 accuracy
114 篇 convolutional ne...
79 篇 deep learning
60 篇 optimization
56 篇 support vector m...
54 篇 computational mo...
51 篇 internet of thin...
51 篇 predictive model...
38 篇 real-time system...
38 篇 data models
36 篇 long short term ...
35 篇 training
34 篇 machine learning
31 篇 classification a...
27 篇 visualization
26 篇 security
25 篇 prediction algor...
25 篇 measurement
25 篇 adaptation model...

机构

211 篇 department of co...
139 篇 department of el...
117 篇 the islamic univ...
99 篇 nitte meenakshi ...
88 篇 school of comput...
86 篇 the islamic univ...
58 篇 sr university sc...
54 篇 college of techn...
44 篇 school of comput...
40 篇 department of co...
36 篇 department of ec...
33 篇 nitte meenakshi ...
33 篇 nitte meenakshi ...
31 篇 nitte meenakshi ...
29 篇 department of ar...
28 篇 school of comput...
26 篇 east west instit...
26 篇 department of co...
26 篇 sr university sc...
25 篇 department of co...

作者

75 篇 pareek piyush ku...
74 篇 piyush kumar par...
25 篇 ramachandra a.c.
23 篇 muntather almusa...
23 篇 almusawi muntath...
22 篇 ramachandra a c
21 篇 parameshachari b...
20 篇 myasar mundher a...
20 篇 srinivas aluvala
20 篇 aluvala srinivas
19 篇 al-jawahry hassa...
18 篇 alabdeli haider
18 篇 hassan m. al-jaw...
18 篇 adnan myasar mun...
18 篇 haider alabdeli
17 篇 mohammed i. habe...
16 篇 mohammed al-faro...
16 篇 nalini n.
15 篇 sundaram s. meen...
15 篇 alzubaidi laith ...

语言

721 篇 英文
546 篇 其他
2 篇 中文

检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

共 1267 条记录，以下是831-840 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 Denoising Convolutional Auto Encoder with Bi-LSTM for Corrupted ECG Signals Reduction

A Denoising Convolutional Auto Encoder with Bi-LSTM for Corr...

引用

Data science and Network Security (ICDSNS), International Conference on

作者： Zaid Alsalami E G Satish M. PonniBala J. Rajalakshmi C. Sushama Department of Computers Techniques Engineering College of technical engineering The Islamic university Najaf Iraq Dept. of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Velalar College of Engineering and Technology Erode India Dept. of CSE School of Computing Mohan Babu University Tirupati India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

The IoT based scheme e-health uses some measurable device for to monitor the health condition of our body. The long-term measurable system like cardiovascular, electrocardiogram (ECG) are significant methods to diagnose the patient's diseases. The drawbacks of this measurable system such as high computational cost, consume more time for training and ECG signals contained some noise and corrupted signals. To addresses this kind of problem, the Denoising Convolution Auto Encoder (DCAE) with Bidirectional Long Short-Term Memory (Bi-LSTM) method is implemented. The DCAE contains encoder and decoder part to convert the ECG corrupted signals into compressed form. Bi-LSTM attached to encoder part that carries the input signals and trains that. Bi-LSTM carries double layers, which makes the training process easily. This proposed method tested on MIT-BIH databases. The advantages of this method, auto encoder easily convert the corrupted signals without using any parameters and reduce computation cost. Bi-LSTM reducing the training process, time consumption and it consisted of double layers, which increase the compression process of signals fastly. The developed DCAE-Bi-LSTM gives better output rate of 97.85% which is better than other existing methods like CNN-LSTM and SVM.

关键词： Training Support vector machines Convolution Computational modeling Noise reduction Noise Electrocardiography Decoding Timing Noise measurement

来源：评论

学校读者我要写书评

暂无评论

Joint Channel Estimation and Feedback in Multiple-Input Multiple-Output Systems by using Pilot Feedback Network with Adaptive Compression Scheme

Joint Channel Estimation and Feedback in Multiple-Input Mult...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Haeedir Mohameed Vidyadevi G Biradar K. Murugan Sakthivel Subramaniam Thomas Koilraj Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Sona College of Technology salem India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Nowadays, Massive Multiple-Input Multiple-Output (MIMO) has become important in 5G which enhances channel capacity by developing large antenna arrays at the Base Station (BS). Traditional approaches had faced several challenges which include high pilot overhead and increased computational complexity. Therefore, this research proposes Pilot Feedback Network with Adaptive Compression Scheme (PFnet with Adaptive Compression Scheme) for Channel Estimation (CE) and feedback in MIMO systems. Initially, data is generated from COST 2100 model. Then, a massive MIMO system with OFDM subcarriers and N_t antennas are considered. For channel estimation, the Least Squares (LS) estimator with interpolation is employed because of its low complexity by making it suitable for scenarios where computational efficiency is prioritized. However, to enhance estimation accuracy, a deep learning-based Channel Estimation and Feedback Network (CEFnet) is integrated by improving the robustness of estimation. Unlike conventional deep learning-based CSI compression methods like CsiNet, PFnet directly compresses pilot signals rather than CSI by reducing overhead. The proposed Adaptive Compression Scheme further optimizes feedback efficiency by dynamically adjusting the compression ratio based on the Signal-to-Noise Ratio (SNR). The proposed PFnet with Adaptive Compression Scheme achieved better results in terms of Normalized Mean Square Error (NMSE) when compared with existing PFnet.

关键词： Adaptation models Adaptive systems Costs Transmitting antennas Channel estimation Receiving antennas Estimation Massive MIMO Data models Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

An Automated Sleep Stage Classification for Healthcare Monitoring by using Single Channel EEG Signal

An Automated Sleep Stage Classification for Healthcare Monit...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sasi Kumar Gurumoorthy D T Chethana M Ramesh B.P Upendra Roy C Sapna Kumari Department of Computer Science and Engineering J.J.College of Engineering and Technology Trichy India Department of Computer Science & Engineering GITAM School of Technology GITAM University India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Channabasaveshwara Institute of Technology Tumkur India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

It is well-established that biomedical signals convey crucial data regarding the functioning of living systems. The physiological and clinical information included in these signals can be improved with adequate processing. Modern qualitative and quantitative analyses of physiological systems and events rely on digital signal processing and pattern recognition methods. Analysis and interpretation of a medical practitioner's signal carry the weight of the analyst's knowledge and expertise, yet such analysis is inherently subjective. If done logically, computer analysis of biomedical information might provide credibility to the expert's interpretation by providing an objective second opinion. Furthermore, it allows for enhanced diagnosis and online monitoring of critically ill patients. The current research intends to develop effective methods for utilizing sleep-monitoring health gadgets. When it comes to handling complex classification or pattern recognition issues, the Support Vector Machine (SVM) is the instrument of choice. In this article, we focus on using support vector machines (SVMs) to identify and categorize apnea. When compared to other methods of categorization, such as sophisticated statistical approaches, SVM performed better. Both an adaptive classification model and a novel approach to merging the decisions of ensemble-based classification models are proposed in the work. The current method relies on an ensemble classifier system and a huge number of features, making it both effective and trustworthy.

关键词： Support vector machines Adaptation models Time-frequency analysis Sleep Statistical analysis Feature extraction Brain modeling

来源：评论

学校读者我要写书评

暂无评论

Chaotic Grey Wolf Optimization for Energy-Efficient Clustering and Routing in Wireless Sensor Networks

Chaotic Grey Wolf Optimization for Energy-Efficient Clusteri...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Muntather Almusawi Gobinath Ravindran Parameshachari B D B. Bhasker Lavanya N L The Islamic University Najaf Iraq Department of Civil Engineering SR University Warangal Telangana India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computing and Information Technology REVA University Bengaluru India Computer Science and Engineering Sapthagiri College of Engineering Bengaluru India

Wireless Sensor Networks (WSNs), the imperative challenge of energy efficiency takes center stage. This paper delves into inventive methodologies to combat energy depletion in sensor nodes, introducing the Chaotic Grey Wolf Optimization Algorithm (CGWOA) for optimal Cluster Head (CH) selection in WSNs. An evolution strategy and a mutation operation operator, CGWOA aims to amplify energy efficiency, scalability, and minimize network overhead through streamlined clustering. The outcomes underscore CGWOA's potential as a solution for advancing energy-efficient clustering and routing in WSNs, particularly in optimizing CH selection. The ensuing numerical validation underscores CGWOA's revealing an energy consumption of 1100 J, a throughput ratio of 69,785 kbps, 4900 active nodes, and a network lifetime extension to 8021. This comparative analysis shows the emphasis on problems to overcome existing methods like Particle Swarm Optimization for Energy-Efficient Clustering (PSO-EEC), Adaptive Hybrid Clustering Scheme with Grey Wolf Optimization (AHCS-GWO), Hybrid Optimization for Cluster Head Selection (HOCK).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Crop Classification on Hyperspectral Images Using Residual Network and Multi-Head Self-Attention Feature Extraction

Crop Classification on Hyperspectral Images Using Residual N...

引用

Data science and Network Security (ICDSNS), International Conference on

作者： N. Naga Saranya V. Vivekanandhan Mohammed Al-Farouni A V Shreyas Gotte Ranjith kumar Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India Department of Computer Science and Engineering Malla Reddy College of Engineering Secunderabad Hyderabad India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Nowadays, hyperspectral imaging has empowered classification and prediction in many fields such as agriculture, land usage, tourism, etc. due to advancements in deep learning. This research focuses on obtaining efficient crop classification using Residual Network, Multi-Head Self- Attention, Convolutional Neural Network (ResNet-MHSA- CNN) approach. The Adaptive Wiener Filtering (AWF) is applied in preprocessing to eliminate noise, handles blurred parts of images effectively and decreases error rate in inverse filtering. The Residual Network (ResNet) method is employed by implement residual learning to eliminate degradation issues of deep neural networks. The high-resolution depth features of hyperspectral images are extracted by residual learning ResNet network. The Multi-Head Self-Attention (MHSA) utilized to capture long-range dependencies and global context information and helps the model in learning diverse patterns. Finally, the extracted features are given to Convolutional Neural Network (CNN) with complex pattern learning ability that excels in image classification with multiple layers of filters. The proposed ResNet-MHSA-CNN approach is evaluated with Indian Pines dataset and achieved 99.74%, 99% accuracy and kappa value respectively. The proposed method outperformed state-of-art methods such as Cross-Mixing Residual Network of Convolutional Neural Network (CMR-CNN), Deep Stacked Denoising Autoencoder (DSDA).

关键词： Degradation Accuracy Filters Error analysis Noise Crops Feature extraction Convolutional neural networks Residual neural networks Hyperspectral imaging

来源：评论

学校读者我要写书评

暂无评论

Autoencoder based Temporal Convolutional Network for Anomaly Detection in Data Streams

Autoencoder based Temporal Convolutional Network for Anomaly...

引用

Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： K. Tamilselvan Ramee RiadHwsein S. meenakshi Sundaram K. S. Vigneshwaran Abhinaya S Department of Information Technology AVS Engineering College Salem India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Nowadays, the rapid growth of data stream from different sources has needed the development of effective Anomaly Detection (AD) methods. The existing unsupervised Deep Learning (DL) approaches learnt various patterns and relationships in data streams without labeled data. However, detecting anomalies in data streams led to significant challenges such as handling high-dimensional data and capturing temporal dependencies. Hence, this research proposes an Autoencoder based Temporal Convolutional Network (AE-TCN) for AD in data streams. Initially, the input data is collected from Numenta Anomaly Benchmark (NAB) repository and further preprocessed with Regression Imputation (RI) to handle missing values and logarithmic scaling to normalize the data respectively. After that, Robustly Optimized Bidirectional Encoder Representation from Transformer approach (RoBERTa) is employed to extract the semantic features. Finally, AE-TCN is introduced to identify the anomalies in data streams by extracting temporal features from reconstructed nominal patterns. From the results, the proposed AE-TCN established high results in terms of Accuracy (Acc), F1-score, and Area Under Curve (AUC) of 99.89%, 94.06%, and 99.85% respectively when compared with existing Deep Autoencoder Neural Network with Adamax Optimization (DANN-AO).

关键词： Autoencoders Semantics Data preprocessing Feature extraction Transformers Imputation Real-time systems Convolutional neural networks Anomaly detection Optimization

来源：评论

学校读者我要写书评

暂无评论

Wafer Surface Semiconductor Defect Classification Using Convolution Neural Network Based Improved Faster R-CNN

Wafer Surface Semiconductor Defect Classification Using Conv...

引用

Data science and Information System (ICDSIS), IEEE International Conference on

作者： Praveenkumar Babu Mohammed I. Habelalmateen Ramya Srikanteswara R. Archana Reddy N. Purushotham Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Engineering SR University Warangal Telangana India Dept. of CSE School of Computing Mohan Babu University Tirupati India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Defect classification helps ensure the quality and reliability of semiconductor devices. Even a minor defect can lead to significant performance issues or failures in electronic components. Moreover, a recurring issue in this field is the identification of minute fault patterns, frequently measuring just a few $\mu \mathrm{m}$ in size, and pixels among enormous volumes of high-resolution imaging. In this paper, proposed the improved Faster Region-Based Convolutional Neural Network (R-CNN) by utilizing the Res2Net model can assign the backbone of the proposed model. The model can train and validating on WM811 K dataset. The dataset contains imbalance classes in the presented categories. Initially the data-augmentation technique is employed to create the synthetic data, and it solve the classes imbalance issue. Subsequently Build the Convolutional Neural Network (CNN) foe classify the categories of the defects by using the extracted features. The Res2Net is effectively extract the various features and the feature pyramid network is used to fuse the extracted information. The proposed method is evaluated and compared with various existing methods and metrices such as Accuracy of $97.86 \%$, precision of $96.50 \%$, Recall of $96.80 \%$ and $F 1$-score of $97.85 \%$ and the methods are namely Deep Convolutional Neural Network for Wafer Defect Identification (DCNN-WDI), ShuffleNet-v2-CNN and Transfer Learning Using Faster R-CNN.

关键词： Semiconductor device modeling Accuracy Volume measurement Transfer learning Feature extraction Data models Numerical models

来源：评论

学校读者我要写书评

暂无评论

A Bidirectional Encoder Representations from Transformers – Multi Head Attention for Predicting the Stock Market Volatility Based on Textual Sentiment Analysis

A Bidirectional Encoder Representations from Transformers –...

引用

Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： G. Anjali Muntather Almusawi N. Zahira Jahan M. Mumjitha S meenakshi Sundaram School of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Maisammaguda Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Applications Nandha Engineering College (Autonomous) Erode India Department of Chemistry New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Recent days, stock market plays an important role in country's economic landscape, serving as a vital conduit for companies and investors to facilitate investments. As a cornerstone of economic growth. The stock market offers unparalleled opportunities for businesses to access capital and expand their operations. The existing approaches for stock market prediction based on textual sentiment analysis has gradient vanishing problems and limited parallelization. Therefore, this paper proposes Bidirectional Encoder Representations from Transformers (BERT)- Multi Head Attention which effectively enabled parallel processing and the transformer minimizes the gradient vanishing problem. Initially, the data is taken from Xueqiu investors community and the processing of text is done in three steps they are web crawling, data cleaning and data transforming. Then, this data is used for measuring volatility which helps the investors assess market risk and potential losses. Finally, proposed BERT- Multi Head Attention is used for predicting and effectively enabled the parallel processing and minimizes the problem of vanishing gradient. The proposed BERT- Multi Head Attention achieved better results in terms of accuracy of 90.65% and Fl-Score of 88.76% when compared with existing NB.

关键词： Support vector machines Sentiment analysis Head Accuracy Parallel processing Streaming media Transformers Loss measurement Cleaning Stock markets

来源：评论

学校读者我要写书评

暂无评论

Prediction of Employee Attrition Classification and Retention Strategies Using Gradient Boosting with Binary Logistic Regression

Prediction of Employee Attrition Classification and Retentio...

引用

Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Mamatha Bai B G Muntather Muhsin Hassan B. Aruna Sri P. Latha F. Anitha Florence Vinola Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Maisammaguda Hyderabad India Department of Information Technology SSM College of Engineering Namakkal India Department of Mathematics New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

A voluntary or involuntary parting of an employee from an organization is known as and the strategies to maintain an employee are known as Employee attrition (turnover) and retention. Employee attrition and retention are crucial Human Resource (HR) concerns, where attrition impacts the organizations performance, profitability, and productivity. Several methods have been proposed to predict attrition of employees, but the existing methods have overfitting, class imbalance, interpretability, and generalizability problems. To overcome these issues, a novel Gradient Boosting-Binary Logistic Regression approach is proposed to predict the employee's turnover in an early stage. Initially, an IBM-HR dataset is considered as the input for preprocessing, which is performed using Standard Scalar to preserve data distributions. Next, One-hot encoding is assessed for data encoding to convert categorical variable into numerical variables. Followed by, feature extraction using Principal Component Analysis (PCA) for faster training and enhancing the performance of model. Later, Gradient Boosting-Binary Logistic Regression (GB-BLR) is employed for classification to analyze and predict attrition of employee's. The proposed GB-BLR method obtained better performance when compared with other existing Extra Trees Classifier (ETC) method in terms of accuracy, precision, recall, and f1 score of 97.48%, 98.57%, 96.25%, and 98.77% respectively.

关键词： Training Productivity Logistic regression Standards organizations Data preprocessing Organizations Feature extraction Encoding Numerical models Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Sentiment Analysis: Exploring the Power of Deep Learning Techniques in Opinion Mining

Deep Learning for Sentiment Analysis: Exploring the Power of...

引用

Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： L Sindhu M. Rameshkumar Gotte Ranjith Kumar B. N. Manjunath Sapna Kumari C Department of Information Technology Sona College of Technology Salem India Department of Computer Science and Engineering Paavai Engineering College India School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science & amp Engineering R.L. Jalappa Institute of Technology Bangalore India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Sentiment analysis (SA) is a popular method for collecting relevant and arbitrary information from text-based data. To locate, examine, extract reactions, and emotions from the data or states, it applies computational linguistics, biometrics, text analysis, and Natural Language Processing (NLP). A SA model can be developed and improved with the use of the features analysis method. However, it can be difficult to find the best classification methods for this type of data. When compared to current feature-based methods, deep learning models for the analysis of sentiment, are capable of providing precise representation and improved performance. In this paper, the deep learning method was proposed for improving sentiment classification performance with a sophisticated embedding word method and develop a network of Convolutional Neural Network and Long short-term memory (CNN-LSTM). For the purpose of predicting the sentiment of tweets for classification, a large amount of Amazon data is analysed. This study, concentrate on improving sentiment analysis performance by building a CNN-LSTM network and particularized model of deep learning with novel word embedding techniques. The CNN-LSTM model has achieved 98.20% accuracy, 99.86% precision, 99.50% recall, and 98.78% recall in sentiment analysis.

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127页 << < 80 81 82 83 84 85 86 87 88 89 > >>

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：