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

682 篇 会议
11 册 图书
1 篇 期刊文献

馆藏范围

694 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

508 篇 工学
- 243 篇 计算机科学与技术...
- 175 篇 软件工程
- 150 篇 农业工程
- 75 篇 控制科学与工程
- 72 篇 机械工程
- 61 篇 信息与通信工程
- 52 篇 仪器科学与技术
- 21 篇 水利工程
- 20 篇 电气工程
- 20 篇 测绘科学与技术
- 17 篇 电子科学与技术（可...
- 17 篇 地质资源与地质工...
- 16 篇 土木工程
- 15 篇 食品科学与工程（可...
- 14 篇 生物工程
246 篇 农学
- 131 篇 作物学
- 58 篇 农业资源与环境
- 29 篇 植物保护
- 19 篇 园艺学
- 13 篇 畜牧学
110 篇 管理学
- 80 篇 管理科学与工程(可...
- 19 篇 工商管理
- 18 篇 图书情报与档案管...
- 17 篇 农林经济管理
97 篇 理学
- 25 篇 数学
- 21 篇 化学
- 15 篇 地理学
- 15 篇 生物学
- 12 篇 大气科学
19 篇 经济学
- 19 篇 应用经济学
8 篇 医学
5 篇 法学
3 篇 艺术学
2 篇 军事学
1 篇 教育学

主题

23 篇 precision agricu...
18 篇 agriculture
13 篇 internet of thin...
11 篇 crops
9 篇 remote sensing
8 篇 deep learning
8 篇 cloud computing
6 篇 wireless sensor ...
6 篇 soil moisture
5 篇 irrigation
5 篇 real-time system...
5 篇 image processing
5 篇 artificial intel...
5 篇 diseases
5 篇 computer vision
5 篇 gis
5 篇 smart agricultur...
5 篇 temperature sens...
4 篇 simulation and m...
4 篇 rural informatiz...

机构

10 篇 beijing research...
9 篇 national enginee...
5 篇 college of infor...
5 篇 college of engin...
5 篇 natl engn res ct...
5 篇 china agricultur...
4 篇 key laboratory o...
4 篇 chitkara univers...
4 篇 graphic era hill...
3 篇 college of engin...
3 篇 institute of rem...
3 篇 school of comput...
3 篇 jilin univ coll ...
3 篇 institute of agr...
3 篇 china agr univ m...
2 篇 sathyabama insti...
2 篇 beijing research...
2 篇 department of co...
2 篇 the key laborato...
2 篇 graphic era deem...

作者

169 篇 china
102 篇 beijing
99 篇 p.r.china
65 篇 china agricultur...
18 篇 nanchang
16 篇 beijing 100083
15 篇 shandong agricul...
14 篇 ministry of educ...
13 篇 taian
11 篇 daoliang li
10 篇 ministry of agri...
10 篇 east china jiaot...
10 篇 beijing normal u...
10 篇 beijing 100081
9 篇 jiangxi
9 篇 qingdao
9 篇 chongqing
8 篇 nanjing
8 篇 hangzhou
8 篇 wenjiang huang

语言

682 篇 英文
7 篇 其他
5 篇 中文

检索条件"任意字段=2nd IFIP International Conference on Computer and Computing Technologies in Agriculture"

共 694 条记录，以下是131-140 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：

Research of Fractal Compression Algorithm Taking Details in Consideration in agriculture Plant Disease and Insect Pests Image 9th

Research of Fractal Compression Algorithm Taking Details in ...

引用

9th ifip WG 5.14 international conference on computer and computing technologies in agriculture (CCTA)

作者： Deng, Qiao Liu, Chunhong Fu, Liting China Agr Univ Coll Informat & Elect Engn Beijing 10083 Peoples R China Beijing Engn & Technol Res Ctr Internet Things Ag Beijing 10083 Peoples R China

ISBN: (纸本)9783319483542;9783319483535

agriculture plant disease and insect pests is varied and tremendously harmful, so experts are needed to diagnose. But experts can't have the energy and time to the field for the majority of farmers to guide, they can only get the image through remote. However, agriculture plant disease and insect pests image is rich in detail, while transmission bandwidth and storage is limited, it is necessary to compress image to make sure of image quality. This paper proposes an improved method based on Jacquin theory to reduce coding time. Encoded sub block is classified into detailed block and non-detailed block, so we can reduce the encoding time. Experimental results show that, for the agriculture plant disease and insect pests image which is rich in detail, the number of encoded blocks reduces to 31.45 % and the encoding time reduces to 32.9 % of the original one.

关键词： Plant disease and insect pests image Detail Fractal encoding Image compression

来源：评论

学校读者我要写书评

暂无评论

CNN and Random Forest-Based Tomato Disease Urgency Prediction: A Vital Tool for agriculture 2

CNN and Random Forest-Based Tomato Disease Urgency Predictio...

引用

2nd IEEE international conference on Intelligent and Innovative technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Deepak Kukreja, Vinay Srivastava, Prateek Thapliyal, Siddhant Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350306415

The study's main findings and performance metrics, which were obtained after a thorough examination of the classification model, are presented in the abstract. These results are based on the provided table of recall, accuracy, F1-Score, & support values at various Urgency levels. With continuous good precision, recall, or F1-Score values at all Urgency levels, the classification model performs admirably. The model's accuracy and usefulness in making exact predictions while capturing a significant share of real positive results are highlighted by this consistency. The table's support values also show that this data set is evenly distributed, demonstrating a fair representation of each class. The classification model achieves an average F1-Score of roughly 91.91% across various measures, namely Micro Average, Macro Average, and therefore Weighted Average, and achieves an overall accuracy of 98%. The accuracy with which the model can predict the levels of Urgency for plant diseases is highlighted by these data. This outstanding performance has promising agricultural applications, especially in the early identification and categorization of plant illnesses, which are essential for prompt interventions and efficient disease control. The dataset includes 9,096 photos that represent different plant illnesses and situations. This model's overall accuracy of 82.78% demonstrates how well it can foretell various plant diseases & their degree of urgency. Overall, the study's findings significantly advance the science of plant disease diagnostics by pointing at potential real-world applications as well as promising areas for further investigation. © 2024 IEEE.

关键词： agriculture Class Diseases Precision

来源：评论

学校读者我要写书评

暂无评论

Artificial Neural Networks or Disease Diagnosis and Categorization in Citrus Plants 2

Artificial Neural Networks or Disease Diagnosis and Categori...

引用

2nd international conference on Electrical, Electronics, Information and Communication technologies, ICEEICT 2023

作者： Ashwini, S. Shanmuga Prabha, P. Krithika, V. Malathi, P. SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350397635

Plant diseases are the leading cause of lost production and productivity in agriculture, which in turn leads to significant economic expenses and food insecurity. Citrus is a plant genus that may produce fruit that has a high market value and is thus cultivated and harvested in large quantities all over the world. The cultivation of citrus, however, has been significantly hampered by the presence of a number diseases, which has resulted in significant decreases in both productivity and quality. The detection and classification of plant diseases has been greatly aided by the development of machine learning and computer vision in recent years. This improvement came about in recent years. Although machine learning models make use of a wide variety of methods for identification and prediction, it would appear that study into the deep learning (DL) area offers the most potential for boosting accuracy as a result of the confluence of these two fields. This research makes use of a two- stage deep Convolutional Neural Network (CNN) model in order to analyse leaf images for the purpose of identifying plant diseases and classifying citrus diseases. Using a region proposal network, the proposed model will first identify potential target diseased areas, and then it will use a classifier to assign the most likely target area to the right disease class. These two fundamental steps are what make up the model. The model that was proposed has a precision that is averaged at 96% and an accuracy of detection that is 95%. © 2023 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

A Study of Literature on Deep Learning-Driven Sentimental Evaluation in Text 2

A Study of Literature on Deep Learning-Driven Sentimental Ev...

引用

2nd IEEE international conference on Futuristic technologies, INCOFT 2023

作者： Damade, Vinayak Arya, Er. Neha Chandigarh University Department of Computer Science and Engineering Mohali140413 India

ISBN: (纸本)9798350308846

Currently being used in many different industries, including medical, agriculture, and business, DL: deep learning) is a rapid-fire, thrilling area of study in natural language processing and image processing in addition This is primarily because utilizing DL has several benefits, including automated feature extraction, processing a huge number of parameters, and more accurate findings. A brief description of SA, text preprocessing requirements, performance indicators, and the function of DL models in SA are given in this study. This paper's major goal is to examine how the DL method may enhance SA's performance in comparison to conventional machine learning techniques for text-based analysis. Text classification may be used for complicated phrases with two opposing emotions that produce two different feelings about the same occurrence since the DL model is more successful for NLP research. We draw the conclusion that by means of a convolutional neural network methodology is more accurate than other methods after analyzing this body of material. The report also demonstrates that employing the DL approach is not the only way to address mixed emotions, broadening the focus of future studies. © 2023 IEEE.

关键词： algorithmic learning deep computing neural networks sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

computer and computing technologies in agriculture 1

引用

丛书名： The international Federation for Information Processing 259

2008年

作者： Li, Daoliang 编

ISBN: (数字)9780387772530

ISBN: (纸本)9780387772523

This book constitutes the second volume of the proceedings of the First ifip WG 12.5 CCTA 2007 held in Wuyishan, China. The ifip series publishes state-of-the-art results in the sciences and technologies of information and communication. The scope of the series includes: foundations of computer science; software theory and practice; education; computer applications in technology; communication systems; systems modeling and optimization; information systems; computers and society; computer systems technology; security and protection in information processing systems; artificial intelligence; and human-computer interaction. Proceedings and post-proceedings of referred international conferences in computer science and interdisciplinary fields are featured. These results often precede journal publication and represent the most current research. The principal aim of the ifip series is to encourage education and the dissemination and exchange of information about all aspects of computing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Plant Disease Detection and Classification using Emperor Penguin Optimizer (EPO) based Region Convolutional Neural Network (RCNN) 2

Plant Disease Detection and Classification using Emperor Pen...

引用

2nd IEEE international conference on Trends in Quantum computing and Emerging Business technologies, TQCEBT 2024

作者： Shanbhog, Manjula Bhatia, Neha Singh, A Swathi Pavithra, M. School of Sciences Department of Computer Science Delhi NCR India Panipat Institute of Engineering and Technology Department of Information Technology Haryana Panipat India MIT-ADT School of Computing Department of Information and Technology Maharashtra Pune India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India

ISBN: (纸本)9798350384277

agriculture stands as India's most crucial industry, despite grappling with a 35% annual loss in crop yield attributed to plant diseases. Traditionally, the detection of plant diseases has been a laborious process, hampered by insufficient laboratory infrastructure and expert knowledge. Plant disease detection methods that are automated provide a useful way to expedite the labor-intensive process of keeping an eye on large-scale agricultural fields and recognizing disease symptoms as soon as they appear on plant leaves. Current developments in deep learning (DL) and computer vision have highlighted the benefits of creating autonomous models for plant disease identification based on visible symptoms on leaves. In this study, we propose a novel method for detecting and classifying plant diseases by combining the Emperor Penguin Optimizer (EPO) with a Region Convolutional Neural Network (RCNN). The suggested methodology uses EPO to improve the discriminative power of features extracted from plant pictures, allowing for a more robust and accurate classification procedure. The Classification Region Convolutional Neural Network (RCNN) is used to leverage spatial correlations within the image, allowing for exact disease region localization. The goal of this integration is to increase the overall efficiency and dependability of plant disease detection systems. The investigations made use of the well-known PlantVillage dataset, which comprises 54,305 data of different plant disease types in 38 categories. Furthermore, an analysis was carried out in comparison with similar advanced investigations. According to the experiment results, RCNN-EPO outperformed in terms of classification accuracy, achieving 94.552%. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Optimizing Agricultural Productivity: Developing a Smart Crop Recommendation System Using Machine Learning 2

Optimizing Agricultural Productivity: Developing a Smart Cro...

引用

2nd international conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Nokshangthemba Kumar, Saurabh Soloman, Shyam Sunder Jannu Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797 103 India

ISBN: (纸本)9798350366570

Crop recommendation is the crucial aspect of modern agriculture, aiming to assist farmers in selecting the most suitable crops for their land and maximizing yield. In this study, the effectiveness of various preprocessing techniques, grid search optimization and the pipeline class are applied in conjunction with the different machine learning algorithm for the crop recommendation. The accuracy and efficiency of the recommendation process enhancement is aimed by applying techniques including data standardization, relative density normalization, and grid search optimization. Leveraging Machine Learning algorithms within a unified pipeline framework allows for systematic evaluation and comparison of models, ultimately providing farmers with reliable recommendations for crop selection. Machine Learning algorithms Decision Tree, K-Nearest Neighbor, Random Forest, Support Vector Machine, Logistic Regression and Naïve Bayes were applied to predict and optimize agricultural productivity based on the dataset collected from Nagaland state. Among these algorithms, Random Forest outperformed the others, achieving an impressive accuracy of 99.54% in predicting agricultural productivity. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Blockchain based Security Solution for Internet of Drones 2

Blockchain based Security Solution for Internet of Drones

引用

2nd international conference on Innovative Sustainable Computational technologies, CISCT 2022

作者： Maurya, Sudhanshu Niveditha, V.R. Anandapriya, B. Verma, Rohan Mirza, Kashish Tiwari, Mohit School of Computing Graphic Era Hill University Bhimtal Campus Nainital India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Patrician College of Arts and Science Department of Computer Application Chennai India Hemvati Nandan Bahuguna Garhwal University Department of Computer Science and Engineering Uttarakhand Srinagar India Graphic Era Hill University Bhimtal Campus Department of Computer Science and Engineering Nainital India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665474160

Nowadays, Drone usage is increasing faster than before. They have frequently been employed in civil services. In a number of sectors, including transportation, agriculture, building, and emergency response, drones have grown in significance. Drones can operate in places that are inaccessible to humans, collect data more effectively than conventional techniques, and carry out tasks more quickly. With the development of technology and laws, their use is anticipated to increase in the future. Therefore, it is imperative that drones must become smarter, more private, and more secure as they offer data transfer secrecy and reliability. In this paper, we have proposed a blockchain-based security solution model for the Internet of Drone environment. The proposed method is evaluated on a limited number of pretend drones. Here we may easily authenticate the administrator, add new drones, authenticate a drone, delete drones, get data from drones, save it to a blockchain-based secure system, and securely retrieve that information. © 2022 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Utilizing a Hybrid CNN-LSTM Model for Automated Severity Classification of Rice Panicle Blast Disease 2

Utilizing a Hybrid CNN-LSTM Model for Automated Severity Cla...

引用

2nd IEEE international conference on Intelligent and Innovative technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Kukreja, Vinay Sharma, Vikrant Sharma, Rishabh Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350306415

Rice panicle blast is a globally prevalent and detrimental disease that impacts rice cultivation on a large scale. It poses a threat to both the quantity and quality of the rice they obtain. Our work proposes a novel approach utilizing a Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM) deep learning (DL) model to accurately assess the severity of a rice panicle blast. This facilitates the resolution of the issue. This novel approach aims to overcome the limitations of traditional approaches to illness diagnosis. These methods are frequently incorrect and inefficient due to their requirement for extensive effort. For this research, we developed and meticulously trained a hybrid model utilizing a robust dataset of over 15,000 distinctly labeled images. These photographs depicted several stages and issues related to blast illness in rice panicles. Our model is unique due to its utilization of the salient features of CNN for spatial localization in images, and LSTM networks for efficient temporal information processing. This union creates a powerful tool for accurately categorizing illnesses. The CNN-LSTM model achieved a high accuracy rate of 97.97% in correctly predicting the severity of a disease within the range of one to five levels. The model maintained a consistently high level of accuracy, recall, and F1-score across all groups. This demonstrates the robustness and reliability of it. Our model surpasses traditional methods. It remains consistent and precise, demonstrating significant advancements in the field of agricultural disease categorization. This study not only provides an effective method for testing rice panicle blast, but also opens up new opportunities for the utilization of intelligent machinery in agriculture. This approach demonstrates the efficacy of DL techniques in enhancing disease management, resulting in increased agricultural yield and environmentally sustainable farming practices. Subsequent research endeavors may endeavor

关键词： agriculture Security Disease Seriousness Image Processing in Agronomy Precision agriculture

来源：评论

学校读者我要写书评

暂无评论

Research on the Theory and Methods for Similarity Calculation of Rough Formal Concept in Missing-Value Context

Research on the Theory and Methods for Similarity Calculatio...

引用

第四届国际计算机与计算技术在农业中的应用研讨会

作者： Wang Kai Li Shao-Wen Zhang You-Hua Liu Chao School of Information and Computer Science Anhui Agricultural University

ISBN: (纸本)9783642183355

In this paper,for the low similarity computation accuracy of concept in the field of agriculture ontology mapping,formal concept analysis theory and rough set theory are introduced to similarity *** considering attribute hierarchies in concept lattice,the semantic hierarchy of the concepts are weighted differently,and the theory and methods of similarity calculation of rough formal concept in missing-value context is ***,similarity computing model is *** results show the model has a high computational accuracy.

关键词： Research on the Theory and Methods for Similarity Calculation of Rough Formal Concept in Missing-Value Context Concept

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70页 << < 10 11 12 13 14 15 16 17 18 19 > >>

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：