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

524 篇 会议
29 册 图书
5 篇 期刊文献

馆藏范围

558 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

384 篇 工学
- 354 篇 计算机科学与技术...
- 228 篇 软件工程
- 60 篇 信息与通信工程
- 47 篇 控制科学与工程
- 39 篇 电气工程
- 38 篇 生物工程
- 24 篇 生物医学工程（可授...
- 24 篇 安全科学与工程
- 20 篇 机械工程
- 19 篇 交通运输工程
- 17 篇 动力工程及工程热...
- 15 篇 化学工程与技术
- 14 篇 电子科学与技术（可...
- 12 篇 光学工程
137 篇 理学
- 71 篇 数学
- 43 篇 生物学
- 37 篇 统计学（可授理学、...
- 22 篇 系统科学
- 19 篇 化学
- 18 篇 物理学
108 篇 管理学
- 74 篇 管理科学与工程(可...
- 48 篇 工商管理
- 47 篇 图书情报与档案管...
27 篇 医学
- 27 篇 基础医学(可授医学...
- 26 篇 临床医学
- 17 篇 药学(可授医学、理...
24 篇 法学
- 22 篇 社会学
15 篇 教育学
- 15 篇 教育学
12 篇 经济学
- 12 篇 应用经济学
12 篇 农学
- 12 篇 作物学
1 篇 军事学

主题

36 篇 big data
27 篇 machine learning
20 篇 data mining
20 篇 artificial intel...
19 篇 internet of thin...
17 篇 cloud computing
10 篇 support vector m...
10 篇 deep learning
10 篇 computational in...
10 篇 predictive model...
10 篇 decision making
9 篇 computational mo...
9 篇 data models
8 篇 real-time system...
7 篇 blockchain
7 篇 industries
7 篇 machine learning...
7 篇 data analysis
6 篇 reinforcement le...
6 篇 data privacy

机构

6 篇 university of vi...
6 篇 university of mi...
5 篇 department of ma...
4 篇 electronics and ...
3 篇 tohoku univ ctr ...
3 篇 university of sa...
3 篇 center for data-...
3 篇 department of co...
3 篇 tongji universit...
2 篇 university of ai...
2 篇 school of comput...
2 篇 western universi...
2 篇 chongqing univer...
2 篇 new delhi
2 篇 uttaranchal scho...
2 篇 hindustan instit...
2 篇 university tun h...
2 篇 school of econom...
2 篇 department of el...
2 篇 cairo

作者

4 篇 swagatam das
4 篇 snehanshu saha
4 篇 carlos a. coello...
3 篇 yuda emi
3 篇 emi yuda
3 篇 altintas ilkay
3 篇 florentino fdez-...
3 篇 jagdish c. bansa...
2 篇 yuehui chen
2 篇 haijun zhang
2 篇 deepak gerard
2 篇 tianyong hao
2 篇 rozaida ghazali
2 篇 pan yongqiang
2 篇 neil yen
2 篇 nakamura shigena...
2 篇 m. rajmohan
2 篇 sridhar
2 篇 rajeswari d.
2 篇 miguel p. rocha

语言

483 篇 英文
71 篇 其他
6 篇 中文

检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

共 558 条记录，以下是21-30 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：

Assessing the Feasibility and Scalability of Using Spark for Identifying Tip Burn Diseases in Strawberry Leaves 2nd

Assessing the Feasibility and Scalability of Using Spark for...

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Prathyuma, V. Hareesh Teja, S. Suganeshwari, G. Divya, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Centre for Advanced Data Science Vellore Institute of Technology Tamil Nadu Chennai6000127 India

ISBN: (纸本)9789819995233

Tip burn is a common calcium deficiency symptom found in strawberry leaves, which can deform, fold over, and discolor the tips or edges of budding leaves. To help farmers detect diseases early and manage their crops more effectively, this work aims to build a solution that employs image processing and machine learning techniques to detect tip burn in strawberry leaves on a farm. The proposed work utilizes a dataset of 1431 images of ‘diseased’ and ‘healthy’ strawberry leaves. A Random Forest classifier, which is an ensemble learning model built on decision trees, CNN, or convolutional neural networks, and VGG-16, a transfer learning-based approach were all fitted in the PySpark environment to assess the feasibility and scalability of using Spark for distributed computing. The results indicate the deep learning approaches outperformed the traditional machine learning approach in accuracy and execution speed, making them a promising tool for identifying tip burn diseases in strawberry leaves. Overall, this work provides farmers with a fast, feasible, and reliable method for detecting tip burn, aiding in crop management, and reducing crop loss. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Exploring the Deep Learning Techniques in Plant Disease Detection: A Review of Recent advances 2nd

Exploring the Deep Learning Techniques in Plant Disease Dete...

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Singh, Saurabh Katarya, Rahul Delhi Technological University New Delhi India

ISBN: (纸本)9789819995202

In agriculture, protecting crop yield is one of the most critical aspects of avoiding crop waste and ensuring food security around the world. One of the most critical aspects of preserving yield is protecting it from pests and plant diseases. With the advancement in the field of Artificial Intelligence (AI), it has been applied to different domains, and one such field is agriculture, where we can incorporate AI. Deep learning (DL), which is a subset of Artificial Intelligence, has gained lots of attention toward plant disease detection in the present day because of its better accuracy and performance in comparison with other techniques like machine learning (ML), etc. In this paper, we provide a comprehensive review of the current research work by utilizing deep learning for plant disease detection. We study the different models and architectures proposed by different authors and try to identify the pros and cons of the proposed methodology. We also discuss the various datasets that have been used in research work for detecting plant diseases. Finally, we describe the possible challenges in implementing deep learning models and discuss the future roadmap that can be followed by trying to identify the research gaps. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Enhanced Dynamic Remote data Possession Checking Protocol with Unlimited Number of Verifications and Public Verifiability for Ensuring Cloud data Integrity 1

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Pragathy, V.Y. Yamuna Devi, L. Coimbatore Institute of Technology Tamil Nadu Coimbatore India Department of Computer Applications Coimbatore Institute of Technology Coimbatore India

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

Today’s organizations and individuals generate large volumes of data. Since they lack resources, the organizations and individuals outsource their data to remote storages such as cloud storages. The user has no control over the data, since it is outsourced to a remotely administered cloud server. The outsourced data may be corrupted or deleted, intentionally or unintentionally. The user should have some means of ensuring the correctness of the outsourced data. In order to verify the correctness of the outsourced data, an Enhanced Dynamic Remote data Possession Checking (EDRDPC) protocol is proposed in this paper. This EDRDPC protocol avoids the need for accessing the entire data file for each verification by randomly sampling the data blocks during the verification process. The proposed protocol also supports unlimited number of integrity verifications and dynamic data operations. This protocol also supports public verifiability in which any authorized third party auditor can perform the data integrity verification. The performance analysis shows the efficiency of the proposed protocol in terms of computation, communication and storage overheads. The security analysis highlights a notable advantage of the proposed protocol in detecting server misbehavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud storage

来源：评论

学校读者我要写书评

暂无评论

IoT-Enabled Smart Agriculture System Using Cognitive computing 3

IoT-Enabled Smart Agriculture System Using Cognitive Computi...

引用

international conference on advances in computing, Communication and Applied Informatics (ACCAI)

作者： Rajababu, Durgam Boddepalli, Eswararao Survase, Rajesh Bhaskar Pal, Arunabha Dandagala, Sreenivasulu Chakravarthi, M. Kalyan SR Univ Dept EEE Sch Engn Warangal Telangana India Virginia Tech Bradley Dept ECE Blacksburg VA USA Savitribai Phule Pune Univ ES Divekar Coll Varvand Dept Geog Pune Maharashtra India Centurion Univ Technol & Management MS Swaminathan Sch Agr Dept Soil Sci Parlakhemundi 761211 Odisha India Annamacharya Univ Annamacharya Inst Technol & Sci Dept Civil Engn Rajampet 516126 Andhra Pradesh India VIT AP Univ Sch Elect Engn Amaravathi 522237 Andhra Pradesh India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Together, the Internet of Things (IoT) and cognitive computing have revolutionized the agricultural industry by enabling the implementation of intelligent husbandry systems. Investigating the creation and use of an Internet of Things-enabled smart husbandry system that leverages cognitive computing techniques is the aim of this study. The system includes detectors to gather data on environmental factors like crop health, soil temperature, and humidity in real-time. After that, a centralized platform receives these data. This data is processed by algorithms used in cognitive computing to give growers useful insights like optimal irrigation timing and early detection of crop issues. The application of this method raises agricultural operations' productivity and enhances decision-making processes' effectiveness. This study covers the following topics: the system's armature;the choice and incorporation of Internet of Things bias;the use of cognitive computing models;and the assessment of system performance in actual agricultural scripts. The results show how cognitive computing and the Internet of Things (IoT) may revolutionize traditional husbandry methods into data-driven, intelligent husbandry systems.

关键词： IoT smart agriculture cognitive computing sensor networks data analytics precision farming

来源：评论

学校读者我要写书评

暂无评论

Hyperparameters’ Optimization of Convolutional Neural Network Using Diversity-Guided Genetic Algorithms for Image Classification 2nd

Hyperparameters’ Optimization of Convolutional Neural Netwo...

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Somani, Zenith Thangavelu, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819995301

Convolutional Neural Networks (CNNs) are an effective tool for image classification and other computer vision problems. However, getting ideal performance necessitates careful adjusting of hyperparameters, which may be a difficult and time-consuming operation when performed manually. Genetic Algorithms (GAs) are a common approach for hyperparameter optimization;however, they have certain limitations, such as premature convergence and inadequate search space exploration. To overcome these challenges, we present Diversity-Guided Genetic Algorithms (DGGAs), a unique approach for hyperparameter optimisation. DGGAs improve GA performance by introducing diversity preservation mechanisms into the GA architecture. By promoting variation among candidate solutions, DGGAs enable more efficient exploration of the hyperparameter search space, resulting in the discovery of a greater range of high-quality configurations. To test the performance of DGGAs, we performed extensive experiments on the CIFAR-10 dataset, a widely recognized benchmark in computer vision. The experimental results show that our approach achieves competitive accuracy results while maintaining significantly higher diversity among the generated solutions compared to state-of-the-art hyperparameter optimization methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

IoT-Based Plant Disease Detection and Classification: A Study on Learning Models 1

引用

international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Singh, Pramod Kumar Shivhare, Anubhav Raut, Ashwin Kumar, Manish Data Analytics Lab Computer Center-2 Indian Institute of Information Technology Allahabad Uttar Pradesh #4206 Allahabad India

ISBN: (数字)9789819909810

ISBN: (纸本)9789819909803

The agriculture sector faces severe challenges due to variable and unpredictable agro-climatic conditions throughout the globe. Early disease detection and prevention measures help in improving the farm productivity. Internet of Things (IoT)-based monitoring techniques are required to reduce the manual efforts and improve the precision in decision making. Sensor-based Internet of Things (SBIoT) is capable of providing a framework for remote monitoring, and alarm generation in case any ailment is detected in crop. This is helpful in better decision making, information sharing, etc. The present work proposes a SBIoT framework to analyze the farm data for detecting the early stage of plant disease. Further, a comparative study of the various machine and deep learning models is provided over the plant village dataset to evaluate the algorithms on different parameters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Synthetic Code-Mixed data Generation 1

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Shravani, Panakanti Madas, Ravali Monica, Madala Hima Bindu, K. National Institute of Technology Andhra Pradesh Andhra Pradesh Tadepalligudem India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

Code-mixed text is common among non-English users. Code-mixed parallel training data fosters the research of code-mixed text. We propose a phrase-based translation approach to generate a synthetic code-mixed corpus. In this approach, we first parse the sentences of both source and target languages, the parsed output contains the sentences with POS tags given to the words, and sentences divided into phrases with head words identified. Next, we extract head words from the parsed output and determine their alignment to replace them in the target language, producing code-mixed sentences. We later use this synthetically generated code-mixed data to train an NMT to perform machine translation. The proposed methodology is independent of the languages used for the code-mixing;Hindi and English languages are used for the demonstration and evaluation. We compare the proposed approach with an alignment-based approach and a pre-trained model approach. Results have shown that our approach has yielded comparable BLEU scores to both the alignment-based approach and the pre-trained model approach, while effectively resolving the issues associated with both methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

data-Based Time Series Modelling of Industrial Grinding Circuits

Data-Based Time Series Modelling of Industrial Grinding Circ...

引用

international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Inapakurthi, Ravi kiran Mitra, Kishalay Indian Institute of Technology Hyderabad Telangana Hyderabad502285 India

ISBN: (纸本)9789819932498

For accurate prediction of size distribution, production rate and energy requirement, modelling of industrial grinding circuits (IGC) is essential. However, the complex physical and chemical phenomenon makes it difficult to model the IGC using the first principal-based approach. Machine learning approaches are known to handle extreme levels of complexities with just the data of the process provided to them. Least square support vector regression (LS-SVR), which is a class of machine learning technique, is explored in this study to model time series data originated from IGC. The parameters of LS-SVR are tuned in an optimization formulation taking the model requirements of error and parameters of the model into consideration. Model approximation of IGC is facilitated with the help of diverse kernel functions, each of which empowers a priority to each feature of IGC. Experimental simulations indicate that Pareto optimal LS-SVR models can be obtained in real-time enabling real-time modelling and control of IGC. To show the competitiveness of the proposed technique, one model from this set is selected to show the prediction performance of the LS-SVR model. © 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

Comparison of data Collection Models in an intelligent Tutoring System for the Inclusive Education of the Learning-Disabled

Comparison of Data Collection Models in an Intelligent Tutor...

引用

international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Dutt, Sarthika Ahuja, Neelu Jyothi COER University Roorkee India Dehradun India

ISBN: (纸本)9789819909803

intelligent tutoring systems were developed to provide effective learning and educate learners without the human interventions. However, identifying learning disabilities is a challenge that requires an effective data collection process. As, the process of data collection affects the generated learner’s details and their learning outcomes in an intelligent tutoring system. Therefore, in the present work, we have compared the data collection process to record the response of the learning-disabled learners for the developed ITS. Also, the machine learning (ML) techniques were explored to extract and select the features related to learning disabilities (dyslexia, dysgraphia, and dyscalculia). This study includes comparison of some popular and effective feature selection and classification algorithms. The feature selection methods utilized in the present study are relief, XGBoost (XGB), elastic net (EN), least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GDBT). The classification algorithms that have been compared based on these feature selection techniques for learning disability prediction are K-nearest neighbors (KNN), logistic regression (LR), linear discriminant analysis (LDA), classification and regression trees (CART), Naïve Bayes (NB), and support vector machine (SVM). The result proves both data collection methods were equally effective and accuracy of CART with the feature selection method LASSO yields highest performance for feature selection and classification process. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Enhancing intelligent Video Surveillance: Deep Learning Approaches for Human Anomalous Behavior Recognition 1

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Prabha, B. Nagaraj, J. Hemanth, Akula Viswanath, Atmakuri Kasi Gadde, Bharath Suravarapu, Sowmithri Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus Chennai India Computer Science and Engineering Madanapalle Institute of Technology and Science Andhra Pradesh Madanapalle India Computer Science and Engineering Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

Modeling anomalous behavior pattern has become as a significant research domain in the recent years due to the security demands in public places. In literature some of the existing approaches such as statistical-based, density-based are applied for pattern detection whereas traditional approaches may not suitable for all scenarios, since they are limited with their properties. In this study, we propose deep learning-based behavior recognition to characterize the abnormality through train and test the frame. Proposed system used the AlexNet Convolution Neural Networks (CNN) architecture, which has largely trained dataset used for feature extraction. In addition, optical flow applied to estimate the human motion. Moreover, motion influence map utilized to reflects the characteristics of the human motion. The CNN-based proposed system can steadily outperforms abnormal behavior detection, especially for the lightweight models when given a small among of training samples. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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56页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：