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

361 篇 会议
42 篇 期刊文献
3 册 图书

馆藏范围

406 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

189 篇 工学
- 157 篇 计算机科学与技术...
- 131 篇 软件工程
- 54 篇 信息与通信工程
- 32 篇 控制科学与工程
- 20 篇 生物工程
- 19 篇 电气工程
- 19 篇 网络空间安全
- 18 篇 生物医学工程（可授...
- 13 篇 材料科学与工程（可...
- 13 篇 化学工程与技术
- 12 篇 电子科学与技术（可...
- 11 篇 农业工程
- 11 篇 安全科学与工程
- 10 篇 交通运输工程
- 8 篇 动力工程及工程热...
92 篇 理学
- 49 篇 数学
- 23 篇 生物学
- 22 篇 统计学（可授理学、...
- 18 篇 物理学
- 16 篇 化学
62 篇 管理学
- 40 篇 管理科学与工程(可...
- 29 篇 图书情报与档案管...
- 22 篇 工商管理
26 篇 医学
- 23 篇 基础医学(可授医学...
- 23 篇 临床医学
- 15 篇 公共卫生与预防医...
- 10 篇 药学(可授医学、理...
14 篇 农学
- 13 篇 作物学
13 篇 法学
- 12 篇 社会学
10 篇 经济学
- 10 篇 应用经济学
4 篇 教育学

主题

25 篇 accuracy
22 篇 support vector m...
21 篇 machine learning
18 篇 feature extracti...
17 篇 predictive model...
12 篇 data mining
12 篇 market research
12 篇 machine learning...
11 篇 internet of thin...
11 篇 face recognition
11 篇 computational mo...
11 篇 cloud computing
11 篇 convolutional ne...
10 篇 deep learning
9 篇 medical services
9 篇 random forests
9 篇 artificial intel...
9 篇 training
8 篇 crops
8 篇 real-time system...

机构

45 篇 department of ma...
39 篇 chandigarh schoo...
20 篇 department of co...
19 篇 department of ma...
18 篇 vivekananda glob...
16 篇 lovely professio...
16 篇 chandigarh schoo...
10 篇 master of comput...
10 篇 master of comput...
9 篇 department of ma...
8 篇 department of co...
7 篇 school of comput...
7 篇 sanskriti univer...
7 篇 radiology techni...
6 篇 department of ap...
6 篇 sanskriti univer...
6 篇 master of comput...
6 篇 hilla university...
6 篇 department of me...
6 篇 sardar patel ins...

作者

13 篇 v. asha
11 篇 asha v.
10 篇 mita nasipuri
6 篇 shiladitya chowd...
6 篇 v asha
6 篇 s p sreeja
6 篇 jamuna kanta sin...
5 篇 kanakia harshil ...
5 篇 dipak kumar basu
5 篇 arpana prasad
5 篇 vincent balu
5 篇 ram sarkar
5 篇 harshil t. kanak...
5 篇 samir malakar
4 篇 ali albawi
4 篇 sonia
4 篇 singh kamred udh...
4 篇 chowdhury shilad...
4 篇 tripathy ramaman...
4 篇 tajpal

语言

386 篇 英文
20 篇 其他

检索条件"机构=Master in Computer Application"

共 406 条记录，以下是1-10 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：

Leveraging Artificial Intelligence and Machine Learning for Disease Diagnosis in Healthcare 1

Leveraging Artificial Intelligence and Machine Learning for ...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Gupta, Aniket Kumar Nidhi, Nidhi Kumari, Shruti Galgotias University Master Of Computer Application Greater Noida India

ISBN: (纸本)9798350356816

This research paper explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in disease diagnosis in healthcare systems. It reviews the current landscape of AI and ML technologies, their integration into diagnostic processes, and their impact on accuracy, efficiency, and patient outcomes. The paper also discusses challenges, ethical considerations, and prospects of AI and ML in disease diagnosis. The study evaluates the performance of a system integrating pulse measurements and user studies to assess its effectiveness in improving user engagement and satisfaction. The results suggest that the proposed system can enhance voice assistant capabilities and contribute to users' overall well-being through continuous health monitoring. Voice assistants can provide virtual healthcare services, such as answering health questions, providing information about symptoms, and offering general health advice. Random Forest (RF) outperforms Logistic Regression (LR) and Support Vector Machine (SVM) with an accuracy rate of 98%. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Enhanced Sentiment Analysis of Airline Twitter Review Using Hybrid Machine Learning and Deep Learning Models 1

Enhanced Sentiment Analysis of Airline Twitter Review Using ...

引用

1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Premasudha, B.G. Patil, Vinayak Siddaganga Institute of Technology Department of Master of Computer Application Tumkur India

ISBN: (纸本)9798350376517

Understanding public opinions and trends requires effective sentiment analysis of social media data. This research explores advanced approaches for sentiment classification using the Twitter US Airline Sentiment Dataset, which contains 14,640 tweets. We assess the effectiveness of various models, including Long Short-Term Memory (LSTM) networks, Naive Bayes, and Support Vector Machines (SVM), along with different text vectorization techniques: Bag of Words (BoW), TF-IDF, and Word2Vec. The results demonstrate that LSTM networks achieve an impressive accuracy of 94.5%, surpassing SVM (90%) and Naive Bayes (85.6%). Among vectorization methods, both BoW and TF-IDF yield the highest AUC scores at 0.92, while Word2Vec-based techniques provide improved F1-Scores, with W2V-AVG at 0.66 and W2V-TFIDF at 0.65. These findings highlight the superior performance of LSTM and Word2Vec for accurate and balanced sentiment classification. © 2024 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

A Novel Deep Convolutional Neural Network for Alzheimer's Disease Prediction using MRI Scans

A Novel Deep Convolutional Neural Network for Alzheimer's Di...

引用

2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Vavaldas, Tejaswee Prasad, G.N.R. Master of Computer Application Telangana Hyderabad India

ISBN: (纸本)9798350344387

Alzheimer's disease (AD) is a progressive neurological disorder that worsens over time and affects cognitive function and memory, making it difficult to carry out daily tasks. The buildup of amyloid plaques and tau protein tangles in the brain leads to the demise of brain cells and the disruption of neuronal connections, ultimately causing the disease. Common symptoms of AD include memory loss, confusion, and difficulty communicating. The suggested improvement entails employing a deep CNN for the automated extraction of features from MRI scans. Deep learning models have achieved astounding results in a variety of picture categorization tasks in recent years. In my research, I examine how the cutting-edge EfficientNetB0 architecture could be used to achieve high-performance picture classification. EfficientNetB0 has gained significant attention due to its efficient and effective design, making it suitable for resource-constrained environments without compromising accuracy. The proposed approach combines the power of EfficientNetB0 with global average pooling and a dense layer to predict class labels. By leveraging the pre-trained weights of EfficientNetB0, the model benefits from the learned representations of a diverse image dataset, enhancing its ability to generalize to unseen data. Fine-tuning is employed to adapt the model to the image classification task. Results showed that the suggested method worked well, with an overall accuracy of 97% across four courses. This research makes a valuable contribution to the domain of image classification by showcasing the capabilities of the EfficientNetB0 architecture and its potential to achieve high-performance results. © 2023 IEEE.

关键词： Alzheimer's disease (AD) convolutional neural network (CNN) Dementia image classification magnetic resonance imaging (MRI)

来源：评论

学校读者我要写书评

暂无评论

CKD NET: Chronic Kidney Disorders Distinction Model 2

CKD NET: Chronic Kidney Disorders Distinction Model

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Prasad, Arpana Asha, V. Nirmala, A.P. Akash, V. Chethan, S.G. Naik, Chintan S New Horizon College of Engineering Master of Computer Application Bengaluru India

ISBN: (纸本)9798350327533

Habitual Renal complaint is a condition that causes a progressive drop in renal function. It refers to a clinical reality that causes renal dysfunction, indecorous complaint opinion, and treatment can affect in unrecoverable renal complaint with veritably short chances of survival. Artificial Intelligence (AI) technologies equip healthcare providers with a rich set of algorithms to prognosticate the habitual development of conditions. previous information helps to enable visionary corrective measures. This exploration study intends to develop an effective tool for prognosticating CKD using machine learning approaches. A machine literacy model is proposed and presented to address the on-uniform distribution of cases through class balancing, point ranking and analysis, and classifier training. © 2024 IEEE.

关键词： Kidney Disease Machine Learning Neural Network Prediction Module ROC Curve

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of Logistic Regression, Support Vector Machines, and LSTM Networks for Sentiment Classification in Academic Reviews 1

A Comparative Study of Logistic Regression, Support Vector M...

引用

1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Premasudha, B.G. Rampalli, Vijayalaxmi Siddaganga Institute of Technology Department of Master of Computer Application Tumkur India

ISBN: (纸本)9798350376517

In recent years, sentiment analysis has become an important tool for understanding what people think and feel about different topics, especially in education. This study looks at how well Long Short Term Memory (LSTM) networks work compared to traditional methods like Support Vector Machines (SVM) and Logistic Regression for classifying sentiment in text. By analyzing a dataset of 18,901 college reviews, which includes reviews sourced from Kaggle and additional reviews for improved dataset balance, this research evaluates how well LSTM networks can detect nuanced sentiments compared to more conventional models. The study includes steps like cleaning the data, testing and fine-tuning several machine learning methods, and measuring how accurately they capture the contextual nuances in the reviews. Findings reveal that LSTM networks outperform traditional methods, offering enhanced accuracy in capturing the emotional depth and context of the text. This study provides valuable insights into the use of advanced neural network models for sentiment analysis, highlighting their ability to improve the interpretation of user feedback. The findings suggest that incorporating LSTM networks into educational feedback systems could result in more accurate sentiment classification and contribute to improved decision-making processes. © 2024 IEEE.

关键词： Support vector regression

来源：评论

学校读者我要写书评

暂无评论

Driver's Drowsiness Detection System Using Machine Learning

Driver's Drowsiness Detection System Using Machine Learning

引用

2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Kaur, Jaswinder Ponnala, Ramesh Master of Computer Application Telangana Hyderabad India

ISBN: (纸本)9798350344387

The structure focuses on the eyes to prevent accidents. In the proposed system, a video is first recorded by a camera. The camera is placed in front of the driver to better capture the driver's face. To create a 2D image, frames of the video are extracted. Faces are detected in images using histogram of oriented gradients (HOG) and linear support vector machine (SVM) for object detection. In this case, SVM (Support Vector Machine) was used to classify the components of the video input. To do this, we first need to develop a bounding box and classification system to recognize pupils initially. Following facial recognition, it marks facial points on the image, such as eye positions. Eye aspect ratio (EAR) and mouth aspect ratio (MAR) are measured using facial landmarks. These features and machine learning approaches are used to estimate driver fatigue. If it detects drowsiness, it sends an alert to the driver to alert them. This system uses the SVM (Support Vector Machine) approach which is achieving an excellent accuracy score of 92.85%. This approach improves model performance and helps to reliably detect driver fatigue and prevent fatal crashes. © 2023 IEEE.

关键词： Detection System Drowsy EAR Haar Cascade MAR SVM

来源：评论

学校读者我要写书评

暂无评论

Herbaceous Plant Leaf Disease Identification 9

Herbaceous Plant Leaf Disease Identification

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Prasad, Arpana Asha, V. Sreeja, S.P. Dheeraj, P. Ajay Hussain, G. Saber New Horizon College of Engineering Master of Computer Application Bengaluru India

ISBN: (纸本)9798350394474

The precise identification and control of foliar diseases in herbaceous species play a crucial part in maintaining agricultural efficiency and environmental equilibrium. This research tackles the difficulties in pinpointing foliar diseases by merging sophisticated image analysis methods with artificial intelligence algorithms. In this study, a comprehensive collection of over 10,000 high-definition images was amassed, showcasing a wide variety of herbaceous plants afflicted by prevalent diseases such as leaf rust, blight, and powdery mildew. These images underwent a series of feature extraction processes to underscore the disease signatures. We employed and assessed several machine learning frameworks, particularly Convolutional Neural Networks and Support Vector Machines With regard to their precision and operational efficacy. The outcomes suggested that the CNN model excelled, achieving an impressive 95% accuracy rate in pinpointing diseases, thereby surpassing conventional diagnostic approaches. It not only provides a powerful solution for swift and precise disease detection in herbaceous plants but also paves the way for future advancements in automated plant health surveillance systems. The findings help farming methodologies, suggesting a pathway towards enhanced disease control and minimization of crop loss. © 2024 IEEE.

关键词： Disease control

来源：评论

学校读者我要写书评

暂无评论

From Pixels to Pain: The Silent Health Risk Associated with Electronic Device Use 8

From Pixels to Pain: The Silent Health Risk Associated with ...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Alamma, B.H. Smitha, G.V. Khaiyum, Samitha Sanchita Dayananda Sager College of Engineering Dept of Master of Computer Application Bangalore India

ISBN: (纸本)9798331505462

In the field of artificial intelligence (AI), machine learning is a subset that focuses on using data to learn and create prediction algorithms. Electronics controls the flow of electricity for processing, and health entails more than just being free from disease. On the other hand, overusing devices can increase health hazards. The inquiry consists of an empirical study that was carried out in Istanbul, Turkey, with 1147 students, as well as a systematic evaluation of ML applications in mental health within the human-computer interface (HCI) literature. The condition of emotional, psychological, and social well-being that affects a person's thoughts, feelings, and behaviour in day-to-day living is known as mental health. The study examines how teens' mental and physical health are affected by their excessive use of digital gadgets, especially cellphones. In this artical the integration of digital technologies and machine learning (ML) in relation to teenage wellbeing and mental health treatment is examined. The assessment also emphasises the necessity of humancentered and morally sound machine learning applications in mental health services. It also discusses how machine learning (ML) might improve mental health services by improving the identification, diagnosis, and treatment of mental health issues. The results show that teens' psychiatric problems, musculoskeletal discomfort, and sleep disruptions are linked to excessive digital device useThe study comes to the conclusion that while ML and digital technologies have a great deal of promise for enhancing mental health outcomes, there are also important socio-technical issues that require attention. These include worries about model bias, data privacy, and the generalisation of machine learning algorithms. Future studies should concentrate on creating rigorous assessment frameworks and transdisciplinary methods to guarantee the ethical and successful application of ML in mental health services. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of SVM, Decision Tree, Random Forest for Crop Recommendation 2

Comparative Analysis of SVM, Decision Tree, Random Forest fo...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Shende, Aryan Yeole, Shrijeet Kanakia, Harshil T. Salmani, Sakina Sardar Patel Institute of Technology Dept of Master of Computer Application Mumbai India

ISBN: (纸本)9798350327533

A sizable portion of Indians view agriculture as their primary source of income. The cultivation of crops is crucial to the economy. Crop production of poor quality is frequently the result of choosing the incorrect crops for the wrong soil or not knowing enough about the growth potential of various crops. The suggested method, which bases crop suggestion on machine learning, is based on measurements of soil properties that have been previously recorded This method helps to maintain crop health and reduces the likelihood of soil degradation. Numerous elements, among them rainfall. Temperature, pH, N, P, K, and humidity are all analyzed using machine learning techniques including random forests, decision trees, and support vector machines. Based on these analyses, recommendations are provided for cultivating an appropriate crop. © 2024 IEEE.

关键词： Decision Tree Random Forest Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

Youtube Comments Sentiment Analysis 9

Youtube Comments Sentiment Analysis

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Giri, Rushikesh Sirsath, Mihir Kanakia, Harshil T. Sardar Patel Institute of Technology Dept of Master of Computer Application Mumbai India

ISBN: (纸本)9798350394474

Sentiment analysis is a method used to identify and understand user opinions and viewpoints regarding a product or service. YouTube, one of the most popular video-sharing platforms, garners millions of views daily, resulting in a plethora of user comments that hold valuable information for improving video rankings. To extract sentiment from these comments, Machine learning techniques and natural language processing (NLP) are used.. Various attempts have been made to classify sentiment into two (positive or negative), three (positive, negative, and neutral), or multiple (e.g., happy, surprised, sad, angry) classes. This study classifies YouTube comments, investigates sentiment analysis techniques that can be used on them, and offers insightful information useful for data mining and sentiment analysis research © 2024 IEEE.

关键词： Sentiment analysis

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41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：