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

1,195 篇 会议
37 篇 期刊文献

馆藏范围

1,232 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

642 篇 工学
- 551 篇 计算机科学与技术...
- 455 篇 软件工程
- 225 篇 信息与通信工程
- 125 篇 控制科学与工程
- 96 篇 电气工程
- 94 篇 生物工程
- 91 篇 生物医学工程（可授...
- 72 篇 电子科学与技术（可...
- 69 篇 网络空间安全
- 53 篇 光学工程
- 46 篇 安全科学与工程
- 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
- 27 篇 交通运输工程
368 篇 理学
- 226 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
241 篇 管理学
- 166 篇 管理科学与工程(可...
- 97 篇 图书情报与档案管...
- 55 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
25 篇 经济学
21 篇 农学
7 篇 教育学
3 篇 军事学

主题

165 篇 feature extracti...
157 篇 accuracy
108 篇 convolutional ne...
80 篇 deep learning
54 篇 optimization
51 篇 support vector m...
50 篇 predictive model...
49 篇 internet of thin...
42 篇 computational mo...
35 篇 training
34 篇 long short term ...
33 篇 real-time system...
33 篇 machine learning
33 篇 data models
31 篇 classification a...
25 篇 security
25 篇 prediction algor...
25 篇 measurement
24 篇 visualization
24 篇 network security

机构

194 篇 department of co...
135 篇 department of el...
117 篇 the islamic univ...
98 篇 nitte meenakshi ...
87 篇 school of comput...
82 篇 the islamic univ...
58 篇 sr university sc...
53 篇 college of techn...
44 篇 school of comput...
37 篇 department of co...
36 篇 department of ec...
33 篇 nitte meenakshi ...
33 篇 nitte meenakshi ...
31 篇 nitte meenakshi ...
28 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

74 篇 piyush kumar par...
73 篇 pareek piyush ku...
24 篇 ramachandra a.c.
23 篇 muntather almusa...
23 篇 almusawi muntath...
21 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
16 篇 mohammed al-faro...
16 篇 nalini n.
15 篇 sundaram s. meen...
15 篇 alzubaidi laith ...
15 篇 mohammed i. habe...

语言

1,055 篇 英文
177 篇 其他
2 篇 中文

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

共 1232 条记录，以下是281-290 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：

Continuous Integration, Testing Deployment and Delivery in Devops

Continuous Integration, Testing Deployment and Delivery in D...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Naresh, E. Murthy, S.V.N. Sreenivasa, N. Merikapudi, Seshaiah Rakhi Krishna, C.R. Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India SJC Institute of Technology Department of Cse Chikkaballapur India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore Yelahanka India Bgsit Department of Cse India

ISBN: (纸本)9798350359688

The proposed research article focuses on Continuous Integration, Testing, Delivery and Deployment in terms of DevOps environment. DevOps is a combination of development and operations. In a usual work environment, both development and operations of that company work entirely differently where mutual interaction is said to be minimum. Thus, to overcome that, DevOps was introduced, where both development and operations must work together in the same environment. In this article we will discuss what each of these terms means, how they work, why they are important in today's software development life cycle (SDLC), what tools are used for each of them, etc. We will consider a few scenarios where the above-mentioned topics are better than the traditional or the most widely used SDLC methodologies. © 2024 IEEE.

关键词： Integration testing

来源：评论

学校读者我要写书评

暂无评论

Quantum Inspired Marine Predator Algorithm with Long Short-Term Memory for Detection and Classification of Chronic Kidney Disease 2

Quantum Inspired Marine Predator Algorithm with Long Short-T...

引用

2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Srikanth, T. Mohamed, Hassan Jayaprakash, D. Vasanthakumar, G.U. Vijayakumari, G. Maisammaguda Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Information Technology Sona College of Technology Salem India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Chronic Kidney Disease (CKD) is a continual decline of the kidney working time being and it is a progressive condition. The main problem with CKD is, it is asymptomatic in its early stages, this makes the early detection challenging. In order to detect the symptoms of the disease and to figure out the exact stage of the disease advance technologies have been introduced. The traditional methods failed to diagnosing the disease accurately in early stage. The paper proposed a Quantum Inspired Marine Predator Algorithm (QIMPA) in order to predict and classify the chronic kidney disease. This algorithm is employed with the marine predators using Brownian and Levy flights that uses hunting strategies. This strategy works in three phases, firstly, prey’s movements and strategies are noted how it escapes from the predator, then a balanced approach is created between predator and prey. Finally, in this phase, optimization process produces a Levy flight pattern to capture the predator and prey moves at low velocity. Long Short-Term Memory (LSTM) is utilized for the classification, it works by capturing the long-range values of features that enhances the diagnosing kidney disease. The proposed QIMPA model obtained 99.04% for CKD dataset compared to the existing methods. © 2024 IEEE.

关键词： Diseases

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

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

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

ISBN: (纸本)9798350313413

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. © 2023 IEEE.

关键词： Convolutional Neural Network Deep Learning Long-short-term memory (LSTM) Sentiment analysis Word embedding

来源：评论

学校读者我要写书评

暂无评论

Deep Learning approaches for Automated Detection of Fake Indian Banknotes

Deep Learning approaches for Automated Detection of Fake Ind...

引用

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

作者： Harshitha Prakash Ayush Yadav Ushashree P Chandranshu Jha Gopi Kumar Sah Archana Naik Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India

This electronic Counterfeit currency is a major concern for governments, banks, and businesses around the world. The detection of counterfeit currency notes is a challenging task that requires sophisticated techniques to analyse various physical and security features on the notes. Traditional methods of detection are time-consuming and prone to human error. This paper proposes the use of deep learning approaches for the automated detection of counterfeit bank notes. The use of Convolutional neural networks (CNNs) to analyse currency note image features and Recurrent Neural Networks (RNNs) to analyse security features such as serial numbers is investigated. An ensemble of CNNs and RNNs is proposed to improve detection accuracy. The proposed method is tested on a dataset of genuine and counterfeit currency notes. This method outperforms traditional methods of detection in terms of accuracy and precision. Our proposed method can be integrated into existing systems to enhance the security of banknotes and protect against counterfeiting.

关键词： Deep learning Integrated circuits Recurrent neural networks Government Feature extraction Security Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Three-Dimensional Convolutional Neural Network for Wheat Rust Disease Classification 2

Three-Dimensional Convolutional Neural Network for Wheat Rus...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Jagadeeshan, N. Nagendar, Y. Swarnalatha, K. Al-Attabi, Kassem Ramachandra, A.C. Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India Maharaja Institute of Technology Department of Artificial Intelligence and Data Science Thandavapura India The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350382952

In worldwide, the wheat is a significant crop which generates main source of food for numerous peoples. Through, the development of crop productions is vulnerable through various diseases such as bacterial, viral and fungal infections. These type of disease can cause important damage in crops which leads to diminish the yield production and grain quality. This paper proposed a Three-Dimensional Convolutional Neural Network (3D-CNN) for wheat rust disease classification that learns to recognize the pattern and structure of rust disease using convolution filter layers. The CGIAR dataset is used which contains 1486 images and it is pre-processed by gaussian filter which reduces the noise and smoothens the image. Then, the Discrete Wavelet Transform (DWT) is used for feature extraction which works in discrete timespan that outputs in low computational cost. Then, 3D-CNN is used for the classification of wheat rust disease. The performance of 3D-CNN is estimated by accuracy, recall, f1score and precision. The 3D-CNN attains accuracy 99.83%, recall 98.89%, f1score 98.81 % and precision 98.79% when compared to existing techniques like GNet+FERSPNET-50 and Few-shot learning based EfficientNet. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Detection and Classification of Multiple Plant Diseases Using CNN

Detection and Classification of Multiple Plant Diseases Usin...

引用

Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： S Vijaya Shetty N A Hemantha Anand Devaganamath R Ananth Abhishek M Alur Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Bangalore India

Plant diseases can have a significant influence on agricultural productivity, quality, and profitability. Early identification and precise diagnosis of plant diseases are essential for successful disease control, which may reduce the use of pesticides and minimize crop losses. In this study, a machine learning-based approach is developed to identify the diseases of various plants using leaf images. Dataset of leaf images of 14 different crops are collected, including apple, pepper, potato, raspberry, blueberry, cherry, peach, soybean, squash, strawberry, corn, grape, orange, and tomato. The images are preprocessed using techniques such as normalization, resizing, and augmentation, to reduce noise and increase the diversity of the dataset. Convolution neural network (CNN) model is used as the back-end and Flask for the front-end to create a user-friendly web application. An interpretability analysis is conducted to understand the features learned by the model, which can provide insights into the underlying causes of the diseases. The study shows the potential of machine learning approaches for early diagnosis and identification of plant diseases, which can assist farmers in taking prompt action and minimizing crop losses. This approach can be extended to other crops and can be integrated with other technologies, such as drones and sensors, for remote and real-time monitoring of crop health.

关键词：

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 1

Autoencoder based Temporal Convolutional Network for Anomaly...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

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

ISBN: (纸本)9798331529246

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). © 2025 IEEE.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Fake News Detection Using Neural Network

Fake News Detection Using Neural Network

引用

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

作者： Ushashree P Archana Naik Siddarth Gurav Ankit Kumar Chethan S R Madhumala B S Department of Computer Science & Engineering Nitte Meenakshi Institute of Technology Bangalore India

This paper uses capabilities of deep learning algorithms Long Short-Term Memory (LSTM) models for the detection of hoax news. In today's world Fake news has become a major problem, as it can spread quickly and have a significant impact on public opinion. Traditional methods for detecting fake news have relied on fact-checking and manual verification, which are time-consuming and not always effective. With the increasing availability of news articles and social media posts, there is a need for automated methods for detecting fake news. One of the effective ways used to eradicate the fake news is adopting LSTM models that have shown considerable results in a variety of natural language processing tasks, including text classification and sentiment analysis. This paper describes how to use LSTM for fake news detection and evaluates its performance on a news article dataset.

关键词： Deep learning Sentiment analysis Social networking (online) Text categorization Semantics Syntactics Feature extraction

来源：评论

学校读者我要写书评

暂无评论

An Efficient Anomaly Detection using Generative Adversarial Networks based on Minimax Loss Function

An Efficient Anomaly Detection using Generative Adversarial ...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Hussein, Layth Lande, Jayapal Routray, Biswaranjan Sundaram, S meenakshi Vadapalli, V K S K Sai The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Centurion University of Technology and Management Department of Computer Science & Engineering Odisha Paralakhemundi India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India S R K R Engineering Department of It Bhimavaram India

ISBN: (纸本)9798350375442

Recently, Artificial Intelligence (AI) is used as a vital tool for describing characteristics of information in the process of data mining and patterns. In data mining, an Anomaly Detection (AD) is an essential part which helps to determine behavior within the data and helps to detect network intrusion. The existing AD in network traffic has identified outliers in unbalanced datasets and difficult to train the model. Therefore, this paper introduces Generative Adversarial Networks (GAN) based on Minimax Loss Function for detecting anomalies in network traffic. Initially, this paper uses Neural Simulation Language-Knowledge Discovery in Databases (NSL-KDD) dataset which contains attacks and are preprocessed by Principal Component Analysis (PCA) to reduce the dimensionality. After that, these features are processed using the Information Gain (IG) algorithm for eliminating irrelevant features and then anomalies are detected by the proposed GAN-Minimax loss function which effectively identifies outliers, handles imbalanced data and trains the model effectively. The proposed GAN-Minimax resulted in high accuracy as 99.92% when compared with existing K-Nearest Neighbor (KNN) method. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Financial Fraudulent Detection using Vortex Search Algorithm based Efficient 1DCNN Classification 2

Financial Fraudulent Detection using Vortex Search Algorithm...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Somanathan Pillai, Sanjaikanth E Vadakkethil Vallabhaneni, Rohith Pareek, Piyush Kumar Dontu, Sravanthi School of Electrical Engineering and Computer Science University of North Dakota Grand ForksND United States University of the Cumberlands United States Nitte Meenakshi Institute of Technology Department of Aiml Bengaluru India University of the Cumberlands Department of Information Technology United States

ISBN: (纸本)9798350382952

Repeated loan fraud threatens financial stability and drives away clients. Financial and banking institutions must immediately recognize network fraud. The banking sector in India and other countries is using AI-driven technology and machine learning algorithms to tackle fraud and unauthorized access. Innovative thinking, increased globalization, and technical developments are raising fraud detection costs. The suggested study would help banks identify credit application fraudsters. The study automated data preprocessing using Kaggle's K-Nearest Neighbor (KNN) algorithm. A one-dimensional convolutional neural network classified. Using the Vortex Search Algorithm (VSA) to fine-tune the classifier hyperparameters improved results. VSA determined the model's hyperparameter sweet spot. The suggested model outperforms other categorization methods with 98.62% accuracy. Better lending banking fraud detection may result from the proposed approach. The VSA-based 1DCNN model detects fraud faster and more precisely. © 2024 IEEE.

关键词： Vortex flow

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124页 << < 25 26 27 28 29 30 31 32 33 34 > >>

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：